Description:Field of the Invention

The disclosure pertains to techniques for evaluating Alzheimer's disease risk in individuals through the collection and analysis of demographic and cognitive data. A novel approach involves processing this data with a Tree Forest Hybrid (TFH) technique, which combines decision trees and random forests for an accurate risk assessment. The results are used to inform healthcare providers or recommend preventive measures for cognitive health maintenance.
Background
The background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
In recent years, the early detection of Alzheimer's disease has gained significant attention due to the progressive nature of the disorder and the lack of a cure. Early identification of risk factors associated with Alzheimer's disease is crucial for timely intervention and management. Conventional approaches for assessing the risk of Alzheimer's disease often rely on standard clinical assessments and neuropsychological tests. These methods include the evaluation of demographic information and cognitive testing scores but may lack the precision and comprehensive analysis needed for accurate risk assessment.
Drawbacks associated with these conventional methods include the potential for subjective interpretation of results and the limitation in their ability to process complex interactions between multiple risk factors. Additionally, the reliance on manual analysis and interpretation of cognitive and demographic data can lead to inefficiencies and delays in the assessment process.
In light of the above discussion, there exists an urgent need for solutions that overcome the limitations associated with conventional methods for assessing Alzheimer's disease risk. The present disclosure addresses these needs by employing a sophisticated Tree Forest Hybrid (TFH) technique to analyze a comprehensive set of demographic and cognitive features for a precise and efficient risk assessment process.
Summary
The following presents a simplified summary of various aspects of this disclosure in order to provide a basic understanding of such aspects. This summary is not an extensive overview of all contemplated aspects, and is intended to neither identify key or critical elements nor delineate the scope of such aspects. Its purpose is to present some concepts of this disclosure in a simplified form as a prelude to the more detailed description that is presented later.
The following paragraphs provide additional support for the claims of the subject application.
The In an aspect, the present disclosure provides a method for detecting Alzheimer's disease risk factor in an individual. This involves collecting input data encompassing demographic features such as gender, age, years of education, and socioeconomic status, alongside cognitive features including Mini-Mental State Examination (MMSE) score, Clinical Dementia Rating (CDR), Estimated Total Intracranial Volume (eTIV), Normalized Whole Brain Volume (nWBV), and Atlas Scaling Factor (ASF) derived from brain imaging data. The method includes processing this input data using a Tree Forest Hybrid (TFH) technique, designed to analyze the demographic and cognitive features and assess the risk of Alzheimer's disease by integrating decision trees with random forests. A positive risk assessment triggers a notification to healthcare providers or caregivers, signaling a potential Alzheimer's disease risk, whereas a negative risk assessment prompts recommendations for preventive measures to maintain cognitive health.
Further enhancements to the method include employing a feature-based analysis approach within the TFH technique that utilizes a set of discriminative features derived from demographic factors and cognitive assessment scores for interpretable classification. Additionally, volumetric measurements derived from brain imaging data are utilized in the assessment process.
In another aspect, a system for assessing Alzheimer's disease risk factor is introduced, comprising a client module for collecting demographic and cognitive features, a server module for processing these features using the TFH technique to assess Alzheimer's disease risk, and an output module located on the client side for displaying the risk assessment result. The server module is further configured to compare the risk assessment result against a database of known Alzheimer's disease benchmarks, enhancing the precision of the assessment. The system includes features such as a graphical user interface for the entry of demographic features, the ability to receive brain imaging data, and the generation of a report summarizing the risk assessment and suggested preventive measures. The preventive measures include lifestyle recommendations tailored to the individual's demographic and cognitive profile, with the TFH algorithm incorporating a weighting mechanism to prioritize demographic or cognitive features based on their predictive importance.

Brief Description of the Drawings

The features and advantages of the present disclosure would be more clearly understood from the following description taken in conjunction with the accompanying drawings in which:
FIG. 1 illustrates a method (100) for detecting Alzheimer's disease risk factors in individuals, in accordance with the embodiments of the present disclosure.
FIG. 2 illustrates a block diagram of a system (200) for assessing Alzheimer's disease risk factors, in accordance with the embodiments of the present disclosure.
FIG. 3 delineates an exemplary system to assess risk of dementia, in accordance with embodiment of present disclosure.

Detailed Description
In the following detailed description of the invention, reference is made to the accompanying drawings that form a part hereof, and in which is shown, by way of illustration, specific embodiments in which the invention may be practiced. In the drawings, like numerals describe substantially similar components throughout the several views. These embodiments are described in sufficient detail to claim those skilled in the art to practice the invention. Other embodiments may be utilized and structural, logical, and electrical changes may be made without departing from the scope of the present invention. The following detailed description is, therefore, not to be taken in a limiting sense, and the scope of the present invention is defined only by the appended claims and equivalents thereof.
The use of the terms “a” and “an” and “the” and “at least one” and similar referents in the context of describing the invention (especially in the context of the following claims) are to be construed to cover both the singular and the plural, unless otherwise indicated herein or clearly contradicted by context. The use of the term “at least one” followed by a list of one or more items (for example, “at least one of A and B”) is to be construed to mean one item selected from the listed items (A or B) or any combination of two or more of the listed items (A and B), unless otherwise indicated herein or clearly contradicted by context. The terms “comprising,” “having,” “including,” and “containing” are to be construed as open-ended terms (i.e., meaning “including, but not limited to,”) unless otherwise noted. Recitation of ranges of values herein are merely intended to serve as a shorthand method of referring individually to each separate value falling within the range, unless otherwise indicated herein, and each separate value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g., “such as”) provided herein, is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention unless otherwise claimed. No language in the specification should be construed as indicating any non-claimed element as essential to the practice of the invention.
Pursuant to the "Detailed Description" section herein, whenever an element is explicitly associated with a specific numeral for the first time, such association shall be deemed consistent and applicable throughout the entirety of the "Detailed Description" section, unless otherwise expressly stated or contradicted by the context.
FIG. 1 illustrates a method (100) for detecting Alzheimer's disease risk factors in individuals, in accordance with the embodiments of the present disclosure. In step (102), incorporates an approach that activates with the collection of essential input data. This data includes demographic features for example gender, age, years of education, and socioeconomic status, along with cognitive features derived from standard assessments and brain imaging data, specifically the Mini-Mental State Examination (MMSE) score, Clinical Dementia Rating (CDR), Estimated Total Intracranial Volume (eTIV), Normalized Whole Brain Volume (nWBV), and Atlas Scaling Factor (ASF). Following step (102) the data collection, the method (100) employs a Tree Forest Hybrid (TFH) technique for processing in step (104). This technique is specially configured to meticulously analyze the gathered demographic and cognitive features. It integrates decision trees with random forests in a unique manner to evaluate the collected data and produce a comprehensive risk assessment result for Alzheimer's disease. Depending on the outcome of this assessment, the method (100) dictates specific actions in step (106). A positive risk assessment, indicative of a potential risk for Alzheimer's disease, triggers a protocol for notifying healthcare providers or caregivers. Conversely, a negative risk assessment, suggesting no significant risk, leads to the recommendation of preventive measures aimed at maintaining cognitive health. This methodological approach facilitates a nuanced and effective strategy for early identification and management of Alzheimer's disease risk factors, emphasizing personalized care and proactive health management strategies.
In an embodiment, the Tree Forest Hybrid (TFH) technique is characterized by a feature-based analysis approach that leverages a set of discriminative features derived from both demographic factors and cognitive assessment scores for interpretable classification. This method (100) of employing the TFH technique enhances the accuracy of Alzheimer's disease risk assessment by incorporating a nuanced analysis of various factors that contribute to the disease's development. The demographic factors include, but are not limited to, gender, age, years of education, and socioeconomic status of the individual. Cognitive assessment scores encompass evaluations such as the Mini-Mental State Examination (MMSE) score, Clinical Dementia Rating (CDR), among others. The discriminative features derived from these factors enable the TFH technique to classify individuals more precisely into risk categories based on their specific profiles. This tailored approach facilitates a more informed decision-making process regarding the necessary interventions or recommendations for preventive measures. By utilizing a feature-based analysis, the TFH technique offers a clear and interpretable classification mechanism, thereby improving the method (100)'s effectiveness in detecting Alzheimer's disease risk factors and providing actionable insights for healthcare providers and caregivers.
In another embodiment, the Tree Forest Hybrid (TFH) technique notably incorporates volumetric measurements derived from brain imaging data into its risk assessment process. This utilization of volumetric measurements enhances the TFH technique's capability to identify Alzheimer's disease risk factors with greater precision. Brain imaging data, including Estimated Total Intracranial Volume (eTIV), Normalized Whole Brain Volume (nWBV), and Atlas Scaling Factor (ASF), provide critical insights into the structural changes in the brain that are indicative of Alzheimer's disease progression. By analyzing these volumetric measurements, the TFH technique can more accurately assess the extent of brain atrophy and other neurodegenerative changes that correlate with Alzheimer's disease risk. This advanced approach allows for a more detailed and nuanced analysis of the cognitive features, significantly contributing to the method (100)'s overall effectiveness in early detection and risk assessment. Consequently, the integration of volumetric measurements from brain imaging data into the TFH technique represents a significant advancement in the method (100)'s ability to assess Alzheimer's disease risk, offering a more comprehensive and accurate evaluation that benefits individuals and healthcare providers alike.
The term "system" as used throughout the present disclosure relates to a framework designed for the assessment of Alzheimer's disease risk factor in individuals. This system, incorporates a client module, a server module, and an output module, each playing a critical role in the risk assessment process.
The term "client module" corresponds to component within the system. This module is engineered to interface with individuals for the collection of essential data. Specifically, the client module is tasked with gathering demographic features, including gender, age, years of education, and socioeconomic status through a user interface. Furthermore, this module is responsible for collecting cognitive features, which encompass Mini-Mental State Examination (MMSE) score, Clinical Dementia Rating (CDR), Estimated Total Intracranial Volume (eTIV), Normalized Whole Brain Volume (nWBV), and Atlas Scaling Factor (ASF), derived from brain imaging data. These features are crucial for assessing the risk of Alzheimer's disease accurately.
The term "server module" is attributed to component of the system. Its primary function is to receive the demographic and cognitive features collected by the client module. Upon receiving this data, the server module processes it using a Tree Forest Hybrid (TFH) technique. This technique involves a sophisticated analysis through the integration of decision trees with random forests, aimed at assessing the risk of Alzheimer's disease. The processed data and the resultant risk assessment outcomes are then stored in a database, ensuring that the information is securely archived for future reference and analysis.
The term "output module" component, situated on the client side of the system. The output module is designed to present the risk assessment results as determined by the server module. Should a potential Alzheimer's disease risk be identified, the output module initiates notifications to healthcare providers or caregivers, alerting them of the detected risk. In scenarios where no significant risk is detected, the output module is configured to provide recommendations for preventive measures. These recommendations are aimed at maintaining or enhancing cognitive health, thereby serving as a proactive approach to managing the well-being of individuals assessed by the system.
FIG. 2 illustrates a block diagram of a system (200) for assessing Alzheimer's disease risk factors, in accordance with the embodiments of the present disclosure. The system (200) is composed of three modules: a client module (202), a server module (204), and an output module (206). Each module is configured to perform distinct functions within the risk assessment process. The client module (202) is designed to interface with the user and is responsible for the collection of input data. Said input data encompasses demographic features, including gender, age, years of education, and socioeconomic status of the individual. Furthermore, said client module (202) collects cognitive features such as Mini-Mental State Examination (MMSE) score, Clinical Dementia Rating (CDR), Estimated Total Intracranial Volume (eTIV), Normalized Whole Brain Volume (nWBV), and Atlas Scaling Factor (ASF) derived from brain imaging data. Configured to receive the collected input data from the client module (202), the server module (204) processes said data using a Tree Forest Hybrid (TFH) technique. Such technique is adept at analyzing the demographic and cognitive features, and assesses the risk of Alzheimer's disease by integrating decision trees with random forests. Additionally, said server module (204) stores the processed data and risk assessment results in a database. On the client side, the output module (206) is configured to display the risk assessment result as determined by the server module (204). Said output module (206) is responsible for triggering notifications to healthcare providers or caregivers if a potential Alzheimer's disease risk is identified, and for providing recommendations for preventive measures to maintain cognitive health when no significant risk is detected.
In an embodiment, the system (200) further comprises an input module that utilizes a graphical user interface (GUI) for the collection of demographic features from the user. This GUI is specifically designed to facilitate an intuitive and user-friendly experience, enabling individuals to input their demographic information, such as gender, age, years of education, and socioeconomic status, with ease. The design and implementation of the GUI are optimized to ensure accuracy and efficiency in data entry, thereby reducing the potential for errors and improving the overall reliability of the data collected. This aspect of the system (200) significantly enhances user interaction, making it more engaging for individuals to participate in the assessment of Alzheimer's disease risk. The inclusion of a GUI in the input module represents a strategic enhancement that underscores the system (200)'s commitment to accessibility and user engagement, contributing to the system (200)’s effectiveness in collecting essential data for Alzheimer's disease risk assessment.
In another embodiment, the system (200) is equipped with the capability to receive brain imaging data through the input module. This functionality allows for the integration of critical cognitive features into the Alzheimer's disease risk assessment process. Brain imaging data, including but not limited to Estimated Total Intracranial Volume (eTIV), Normalized Whole Brain Volume (nWBV), and Atlas Scaling Factor (ASF), are essential for a comprehensive analysis of potential neurodegenerative patterns. The system (200)'s ability to process these volumetric measurements derived from brain imaging data adds a significant dimension to the risk assessment, enabling a more detailed and accurate evaluation of Alzheimer's disease risk. This capability not only leverages advanced medical imaging technologies but also ensures that the assessment encompasses a broad spectrum of relevant data, thereby enhancing the precision of the risk assessment outcomes.
In yet another embodiment, the server module (204) within the system (200) is configured to compare the risk assessment result with a database of known Alzheimer's disease benchmarks. This comparison enables the system (200) to contextualize the risk assessment results by referencing established benchmarks, providing a more nuanced understanding of the individual's risk level. The integration of benchmark comparison into the server module (204)'s functionality adds a layer of validation to the assessment process, ensuring that the risk assessment results are both accurate and reliable. By leveraging historical and epidemiological data on Alzheimer's disease, the server module (204) can offer insights that are deeply informed by existing knowledge, thereby enhancing the system (200)'s ability to identify and categorize risk with greater precision.
In an embodiment, the output module (206) is designed to generate a comprehensive report that summarizes the risk assessment results and suggests preventive measures. This report serves as a tangible output that can be shared with healthcare providers, caregivers, or the individuals themselves, providing a detailed overview of the risk assessment findings and offering actionable recommendations. The ability to generate such a report is crucial for facilitating informed decision-making and enabling proactive health management strategies. By providing a synthesized summary of the risk assessment and recommended preventive measures, the system (200) ensures that critical information is communicated effectively, fostering a collaborative approach to managing Alzheimer's disease risk.
In an embodiment, the system (200) includes preventive measures that comprise lifestyle recommendations tailored to the individual's demographic and cognitive profile. These recommendations are formulated based on the comprehensive analysis of the individual's data, ensuring that they are both relevant and personalized. The inclusion of tailored lifestyle recommendations underscores the system (200)'s holistic approach to Alzheimer's disease risk management, emphasizing not just the identification of risk but also the promotion of interventions that can mitigate this risk. By focusing on lifestyle adjustments that are specifically suited to the individual's unique circumstances, the system (200) empowers individuals to take proactive steps towards maintaining cognitive health.
In another embodiment, the Tree Forest Hybrid (TFH) algorithm incorporated into the system (200) features a weighting mechanism that prioritizes demographic and cognitive features based on their predictive importance. This mechanism enhances the algorithm's analytical capabilities by ensuring that the most significant predictors of Alzheimer's disease risk are given precedence in the risk assessment process. The weighting mechanism allows for a more refined analysis, which is critical for accurately determining the individual's risk level. By intelligently prioritizing the data, the TFH algorithm ensures that the assessment is both targeted and effective, leveraging the full potential of the collected data to provide a nuanced risk evaluation.
FIG. 3 delineates an exemplary system to assess risk of dementia, in accordance with embodiment of present disclosure. The system comprises input data collection, which receives demographic features and cognitive features. Demographic features include four parameters: gender, age, years of education, and socioeconomic status. The cognitive features comprise mini-mental state examination data, clinical dementia rating, estimated total intracranial volume, normalized whole brain volume, and atlas scaling factor. The cognitive features enable assessing the cognitive function and brain structure status, offering a medical and neurological perspective on the individual's condition. The cognitive features and demographic features are processed through the TFH technique to evaluate the risk of dementia. The 'Risk Assessment' module classifies the outcome into one of two possible diagnoses: 'Demented' or 'Non-Demented'. In cases where the result is 'Demented', the system activates the 'Trigger Notifications for Healthcare Providers or Caregivers' module. On the contrary, if the assessment outcome is 'Non-Demented', the system moves towards 'Recommend Preventive Measures', signifying a proactive approach in maintaining cognitive health and possibly preventing or delaying the onset of dementia. The objective (of present disclosure) focus on analyzing critical characteristics from brain imaging data and incorporating diverse discriminative features such as cognitive scores, demographic information, and brain volume measurements. The system of present disclosure utilizes decision trees and random forests to improve classification accuracy and overcome existing diagnostic limitations. Furthermore, the present disclosure provides practical and efficient, advancing clinical applications in the detection and management of Alzheimer's disease.
The described methodology of present disclosure initiates with the collection of input data, encompassing demographic characteristics such as gender, age, educational background, and socioeconomic status, in addition to cognitive parameters including the Mini-Mental State Examination, Clinical Dementia Rating, and brain volume measurements like Estimated Total Intracranial Volume, Normalized Whole Brain Volume, and Atlas Scaling Factor. This data is then processed by the Tree Forest Hybrid (TFH) Algorithm, a sophisticated computational model specifically tailored for analyzing such data to evaluate Alzheimer's disease risk. Upon analysis, the TFH Algorithm executes a risk assessment to determine the individual's cognitive health status: a significant risk leads to a 'Demented' classification, whereas an insignificant risk results in a 'Non-Demented' classification. Consequent to the assessment, the system engages distinct protocols: for individuals classified as 'Demented', it triggers notifications to healthcare providers or caregivers to take appropriate actions; for those classified as 'Non-Demented', it suggests preventive measures aimed at preserving cognitive health and potentially forestalling the progression of Alzheimer's disease.
In an embodiment, the disclosed method (100) for identifying the stages of Alzheimer's disease, enhancing early diagnosis through a feature-based analysis approach that overcome limitations of existing known solution. The THF technique synergizes decision trees and random forests, leading to improved classification accuracy and robustness in detecting cognitive health status. The system utilizes various features such as demographic data and cognitive scores, alongside volumetric brain measurements. The methodology of present disclosure enhances diagnostic precision by utilizing a broad spectrum of features and increases efficiency and practicality over existing Alzheimer's detection methods. Further, the methodology of present disclosure facilitates prompt intervention and customized healthcare management, enabling early identification of at-risk individuals, which is crucial for optimizing treatment plans and improving patient outcomes. Moreover, the methodology of present disclosure provides a proactive healthcare approach that focuses on early detection and preemptive action, significantly impacting patient care and advancing the management of Alzheimer's disease.
Example embodiments herein have been described above with reference to block diagrams and flowchart illustrations of methods and apparatuses. It will be understood that each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations, respectively, can be implemented by various means including hardware, software, firmware, and a combination thereof. For example, in one embodiment, each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations can be implemented by computer program instructions. These computer program instructions may be loaded onto a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions which execute on the computer or other programmable data processing apparatus create means for implementing the functions specified in the flowchart block or blocks.
Throughout the present disclosure, the term ‘processing means’ or ‘microprocessor’ or ‘processor’ or ‘processors’ includes, but is not limited to, a general purpose processor (such as, for example, a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a microprocessor implementing other types of instruction sets, or a microprocessor implementing a combination of types of instruction sets) or a specialized processor (such as, for example, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), or a network processor).
The term “non-transitory storage device” or “storage” or “memory,” as used herein relates to a random access memory, read only memory and variants thereof, in which a computer can store data or software for any duration.
Operations in accordance with a variety of aspects of the disclosure is described above would not have to be performed in the precise order described. Rather, various steps can be handled in reverse order or simultaneously or not at all.
While several implementations have been described and illustrated herein, a variety of other means and/or structures for performing the function and/or obtaining the results and/or one or more of the advantages described herein may be utilized, and each of such variations and/or modifications is deemed to be within the scope of the implementations described herein. More generally, all parameters, dimensions, materials, and configurations described herein are meant to be exemplary and that the actual parameters, dimensions, materials, and/or configurations will depend upon the specific application or applications for which the teachings is/are used. Those skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, many equivalents to the specific implementations described herein. It is, therefore, to be understood that the foregoing implementations are presented by way of example only and that, within the scope of the appended claims and equivalents thereto, implementations may be practiced otherwise than as specifically described and claimed. Implementations of the present disclosure are directed to each individual feature, system, article, material, kit, and/or method described herein. In addition, any combination of two or more such features, systems, articles, materials, kits, and/or methods, if such features, systems, articles, materials, kits, and/or methods are not mutually inconsistent, is included within the scope of the present disclosure.

Claims

I/We claims:

A method (100) for detecting Alzheimer's disease risk factor in an individual, comprising:
a) collecting input data, comprising:
i) demographic features including gender, age, years of education, and socioeconomic status of the individual; and
ii) cognitive features including Mini-Mental State Examination (MMSE) score, Clinical Dementia Rating (CDR), Estimated Total Intracranial Volume (eTIV), Normalized Whole Brain Volume (nWBV), and Atlas Scaling Factor (ASF) based on brain imaging data;
b) processing the collected input data using a Tree Forest Hybrid (TFH) technique configured to:
i) analyze the demographic and cognitive features; and
ii) assess the risk of Alzheimer's disease by integrating decision trees with random forests to produce a risk assessment result; and
c) outputting the risk assessment result, wherein:
i) a positive risk assessment triggers a notification to healthcare providers or caregivers, indicating a potential Alzheimer's disease risk; and
ii) a negative risk assessment prompts recommendations for preventive measures to maintain cognitive health.
The method (100) of claim 1, wherein the TFH technique employs a feature-based analysis approach that utilizes a set of discriminative features derived from both demographic factors and cognitive assessment scores for interpretable classification.
The method (100) of claim 1, wherein the TFH technique utilizes volumetric measurements derived from brain imaging data.
A system (200) for assessing Alzheimer's disease risk factor, comprising:
a) a client module (202) configured to:
i) collect demographic features including gender, age, years of education, and socioeconomic status of the individual via a user interface;
ii) collect cognitive features including Mini-Mental State Examination (MMSE) score, Clinical Dementia Rating (CDR), Estimated Total Intracranial Volume (eTIV), Normalized Whole Brain Volume (nWBV), and Atlas Scaling Factor (ASF) from brain imaging data;
b) a server module (204) configured to:
i) receive the collected demographic and cognitive features from the client module (202);
ii) process the received data using a Tree Forest Hybrid (TFH) technique to analyze the data and assess the risk of Alzheimer's disease by integrating decision trees with random forests;
iii) store the processed data and risk assessment results in a database;
c) an output module (206) located on the client side configured to:
i) display the risk assessment result determined by the server module (204);
ii) trigger notifications to healthcare providers or caregivers if a potential Alzheimer's disease risk is identified;
iii) provide recommendations for preventive measures to maintain cognitive health if no significant risk is identified.
The system (200) of claim 4, wherein the input module utilizes a graphical user interface for the entry of demographic features by a user.
The system (200) of claim 4, wherein the input module receives brain imaging data.
The system (200) of claim 4, wherein the server module (204) is further configured to compare the risk assessment result against a database of known Alzheimer's disease benchmark.
The system (200) of claim 4, wherein the output module (206) is configured to generate a report summarizing the risk assessment and suggested preventive measures.
The system (200) of claim 4, wherein the preventive measures include lifestyle recommendations tailored to the individual's demographic and cognitive profile.
The system (200) of claim 4, wherein the TFH algorithm includes a weighting mechanism to prioritize a list demographic or cognitive features based on their predictive importance.

METHOD AND SYSTEM FOR ALZHEIMER'S DISEASE RISK ASSESSMENT USING TREE FOREST HYBRID ANALYSIS

The present disclosure pertains to a method for assessing the risk of Alzheimer's disease in individuals by collecting and analyzing both demographic and cognitive data. The process involves the gathering of demographic features such as gender, age, years of education, and socioeconomic status, along with cognitive features derived from evaluations like the Mini-Mental State Examination (MMSE) score, Clinical Dementia Rating (CDR), Estimated Total Intracranial Volume (eTIV), Normalized Whole Brain Volume (nWBV), and Atlas Scaling Factor (ASF) based on brain imaging data. This collected data is then processed using a sophisticated Tree Forest Hybrid (TFH) technique, which combines decision trees with random forests to analyze the data comprehensively and assess the individual's risk of developing Alzheimer's disease. Depending on the risk assessment outcome, the method triggers appropriate responses: a positive risk assessment results in notifications to healthcare providers or caregivers about the potential risk, while a negative risk assessment leads to recommendations for preventive measures aimed at maintaining or enhancing cognitive health. This innovative approach facilitates early detection and intervention strategies, potentially improving the quality of life and care for individuals at risk of Alzheimer's disease.

Drawings
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FIG. 1
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FIG. 2
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FIG. 3
, Claims:I/We claims:

A method (100) for detecting Alzheimer's disease risk factor in an individual, comprising:
a) collecting input data, comprising:
i) demographic features including gender, age, years of education, and socioeconomic status of the individual; and
ii) cognitive features including Mini-Mental State Examination (MMSE) score, Clinical Dementia Rating (CDR), Estimated Total Intracranial Volume (eTIV), Normalized Whole Brain Volume (nWBV), and Atlas Scaling Factor (ASF) based on brain imaging data;
b) processing the collected input data using a Tree Forest Hybrid (TFH) technique configured to:
i) analyze the demographic and cognitive features; and
ii) assess the risk of Alzheimer's disease by integrating decision trees with random forests to produce a risk assessment result; and
c) outputting the risk assessment result, wherein:
i) a positive risk assessment triggers a notification to healthcare providers or caregivers, indicating a potential Alzheimer's disease risk; and
ii) a negative risk assessment prompts recommendations for preventive measures to maintain cognitive health.
The method (100) of claim 1, wherein the TFH technique employs a feature-based analysis approach that utilizes a set of discriminative features derived from both demographic factors and cognitive assessment scores for interpretable classification.
The method (100) of claim 1, wherein the TFH technique utilizes volumetric measurements derived from brain imaging data.
A system (200) for assessing Alzheimer's disease risk factor, comprising:
a) a client module (202) configured to:
i) collect demographic features including gender, age, years of education, and socioeconomic status of the individual via a user interface;
ii) collect cognitive features including Mini-Mental State Examination (MMSE) score, Clinical Dementia Rating (CDR), Estimated Total Intracranial Volume (eTIV), Normalized Whole Brain Volume (nWBV), and Atlas Scaling Factor (ASF) from brain imaging data;
b) a server module (204) configured to:
i) receive the collected demographic and cognitive features from the client module (202);
ii) process the received data using a Tree Forest Hybrid (TFH) technique to analyze the data and assess the risk of Alzheimer's disease by integrating decision trees with random forests;
iii) store the processed data and risk assessment results in a database;
c) an output module (206) located on the client side configured to:
i) display the risk assessment result determined by the server module (204);
ii) trigger notifications to healthcare providers or caregivers if a potential Alzheimer's disease risk is identified;
iii) provide recommendations for preventive measures to maintain cognitive health if no significant risk is identified.
The system (200) of claim 4, wherein the input module utilizes a graphical user interface for the entry of demographic features by a user.
The system (200) of claim 4, wherein the input module receives brain imaging data.
The system (200) of claim 4, wherein the server module (204) is further configured to compare the risk assessment result against a database of known Alzheimer's disease benchmark.
The system (200) of claim 4, wherein the output module (206) is configured to generate a report summarizing the risk assessment and suggested preventive measures.
The system (200) of claim 4, wherein the preventive measures include lifestyle recommendations tailored to the individual's demographic and cognitive profile.
The system (200) of claim 4, wherein the TFH algorithm includes a weighting mechanism to prioritize a list demographic or cognitive features based on their predictive importance.

METHOD AND SYSTEM FOR ALZHEIMER'S DISEASE RISK ASSESSMENT USING TREE FOREST HYBRID ANALYSIS