Description:
BACKGROUND
Field of Invention
[001] Embodiments of the present invention generally relate to a disease prediction system and particularly to a system for personalized prediction and precautionary guidance for epilepsy disease using machine learning techniques.
Description of Related Art
[002] Epilepsy is a neurological disorder characterized by recurrent seizures, affecting millions of people worldwide. Despite advances in treatment and management, predicting and preventing epileptic seizures remain significant challenges. Seizure occurrence is highly variable among individuals, making personalized prediction and precautionary guidance essential for effective disease management.
[003] Machine learning techniques has emerged as a powerful tool for analyzing medical data and extracting meaningful insights for disease prediction and management. By leveraging ML algorithms, researchers and clinicians can analyze diverse datasets, including electroencephalogram (EEG) recordings, clinical data, and genetic information, to develop personalized predictive models for epilepsy.
[004] A traditional approach to epilepsy management involves medication adjustment based on seizure frequency and severity. However, this one-size-fits-all approach is not optimal for all patients, as individual responses to medication can vary significantly. Personalized prediction models can help identify specific risk factors and biomarkers associated with seizure onset, enabling targeted interventions and precautionary measures.
[005] The key challenge in developing personalized prediction models for epilepsy lies in the complexity and heterogeneity of the disease. Epileptic seizures can manifest in various forms and frequencies, influenced by factors such as genetic predisposition, environmental triggers, and comorbidities. Capturing this complexity requires sophisticated ML techniques capable of analyzing multi-modal data and identifying subtle patterns indicative of impending seizures.
[006] There is thus a need for an improved and advanced system for personalized prediction and precautionary guidance for epilepsy disease using machine learning techniques that can administer the aforementioned limitations in a more efficient manner.
SUMMARY
[007] Embodiments in accordance with the present invention provide a system for personalized prediction and precautionary guidance for epilepsy disease using machine learning techniques. The system comprising: an input unit adapted to receive user inputs selected from epilepsy disease symptoms, medical history, lifestyle factors, or a combination thereof. The system further comprising: a processing unit in communication with the input unit. The processing unit is configured to: receive the user inputs from the input unit; extract features from the received user inputs using preprocessing techniques; apply a plurality of machine learning models based on the extracted features to predict a likelihood of epileptic seizures by each of the plurality of machine learning models; conduct a comparative analysis of the predicted likelihood of the epileptic seizures by each of the plurality of machine learning models; perform classifier identification to determine a final likelihood of the epileptic seizures; and generate the personalized prediction and precautionary guidance based on the final likelihood of epileptic seizures.
[008] Embodiments in accordance with the present invention further provide a method for personalized prediction and precautionary guidance for epilepsy disease using machine learning techniques. The method comprising steps of: receiving user inputs from an input unit; extracting features from the received user inputs using preprocessing techniques; applying a plurality of machine learning models based on the extracted features to predict a likelihood of epileptic seizures by each of the plurality of machine learning models; conducting a comparative analysis of the predicted likelihood of the epileptic seizures by each of the plurality of machine learning models; performing classifier identification to determine a final likelihood of the epileptic seizures; and generating the personalized prediction and precautionary guidance based on the final likelihood of epileptic seizures.
[009] Embodiments of the present invention may provide a number of advantages depending on their particular configuration. First, embodiments of the present application may provide a system for personalized prediction and precautionary guidance for epilepsy disease using machine learning techniques.
[0010] Next, embodiments of the present application may provide a system for personalized prediction and precautionary guidance for epilepsy disease using machine learning techniques that overcomes data scarcity allowing for the development of more robust predictive models, improving their accuracy and reliability in epilepsy prediction.
[0011] Next, embodiments of the present application may provide a system for personalized prediction and precautionary guidance for epilepsy disease using machine learning techniques that uses models that can better capture the diverse manifestations and underlying causes of the condition, leading to more comprehensive insights and personalized interventions.
[0012] Next, embodiments of the present application may provide a system for personalized prediction and precautionary guidance for epilepsy disease using machine learning techniques that provides tailored predictions and precautionary guidance, ensuring that patient-specific physiological differences are considered in disease management.
[0013] Next, embodiments of the present application may provide a system for personalized prediction and precautionary guidance for epilepsy disease using machine learning techniques that helps prevent bias in predictive models, ensuring that they accurately anticipate both seizure and non-seizure episodes.
[0014] Next, embodiments of the present application may provide a system for personalized prediction and precautionary guidance for epilepsy disease using machine learning techniques that overcomes real-time monitoring difficulties and facilitates the implementation of continuous monitoring using wearable devices, enabling timely intervention and proactive management of epilepsy.
[0015] Next, embodiments of the present application may provide a system for personalized prediction and precautionary guidance for epilepsy disease using machine learning techniques that addresses ethical and privacy concerns and ensures that patient privacy, data security, and potential biases are carefully managed, promoting ethical and responsible use of healthcare data in epilepsy prediction.
[0016] Next, embodiments of the present application may provide a system for personalized prediction and precautionary guidance for epilepsy disease using machine learning techniques that enhances trust among clinicians and patients, leading to better acceptance and adherence to therapeutic recommendations.
[0017] These and other advantages will be apparent from the present application of the embodiments described herein.
[0018] The preceding is a simplified summary to provide an understanding of some embodiments of the present invention. This summary is neither an extensive nor exhaustive overview of the present invention and its various embodiments. The summary presents selected concepts of the embodiments of the present invention in a simplified form as an introduction to the more detailed description presented below. As will be appreciated, other embodiments of the present invention are possible utilizing, alone or in combination, one or more of the features set forth above or described in detail below.
BRIEF DESCRIPTION OF THE DRAWINGS
[0019] The above and still further features and advantages of embodiments of the present invention will become apparent upon consideration of the following detailed description of embodiments thereof, especially when taken in conjunction with the accompanying drawings, and wherein:
[0020] FIG. 1A illustrates a block diagram of a system for personalized prediction and precautionary guidance for epilepsy disease using machine learning techniques, according to an embodiment of the present invention;
[0021] FIG. 1B illustrates a user interface of the system for the personalized prediction and precautionary guidance for epilepsy disease using machine learning techniques, according to an embodiment of the present invention;
[0022] FIG. 1C illustrates a user interface of the system for the personalized prediction and precautionary guidance for epilepsy disease using machine learning techniques, according to another embodiment of the present invention;
[0023] FIG. 1D illustrates a user interface of the system for the personalized prediction and precautionary guidance for epilepsy disease using machine learning techniques, according to another embodiment of the present invention;
[0024] FIG. 1E illustrates a user interface of the system for the personalized prediction and precautionary guidance for epilepsy disease using machine learning techniques, according to another embodiment of the present invention;
[0025] FIG. 2 illustrates a block diagram of a processing unit of the system for the personalized prediction and precautionary guidance for epilepsy disease using machine learning techniques, according to an embodiment of the present invention; and
[0026] FIG. 3 depicts a flowchart of a method for the personalized prediction and precautionary guidance for epilepsy disease using machine learning techniques, according to an embodiment of the present invention.
[0027] The headings used herein are for organizational purposes only and are not meant to be used to limit the scope of the description or the claims. As used throughout this application, the word "may" is used in a permissive sense (i.e., meaning having the potential to), rather than the mandatory sense (i.e., meaning must). Similarly, the words “include”, “including”, and “includes” mean including but not limited to. To facilitate understanding, like reference numerals have been used, where possible, to designate like elements common to the figures. Optional portions of the figures may be illustrated using dashed or dotted lines, unless the context of usage indicates otherwise.
DETAILED DESCRIPTION
[0028] The following description includes the preferred best mode of one embodiment of the present invention. It will be clear from this description of the invention that the invention is not limited to these illustrated embodiments but that the invention also includes a variety of modifications and embodiments thereto. Therefore, the present description should be seen as illustrative and not limiting. While the invention is susceptible to various modifications and alternative constructions, it should be understood, that there is no intention to limit the invention to the specific form disclosed, but, on the contrary, the invention is to cover all modifications, alternative constructions, and equivalents falling within the scope of the invention as defined in the claims.
[0029] In any embodiment described herein, the open-ended terms "comprising", "comprises”, and the like (which are synonymous with "including", "having” and "characterized by") may be replaced by the respective partially closed phrases "consisting essentially of", “consists essentially of", and the like or the respective closed phrases "consisting of", "consists of”, the like.
[0030] As used herein, the singular forms “a”, “an”, and “the” designate both the singular and the plural, unless expressly stated to designate the singular only.
[0031] FIG. 1A illustrates a block diagram of a system 100 for a personalized prediction and precautionary guidance for epilepsy disease using machine learning techniques, according to an embodiment of the present invention. In an embodiment of the present invention, the system 100 may be adapted to predict epilepsy disease and provide guidance to a user for prevention and cure of the epilepsy disease. In an embodiment of the present invention, the system 100 may comprise an input unit 102, a computer application 104, a user interface 106, a database 108, and a processing unit 110.
[0032] In an embodiment of the present invention, the input unit 102 may be adapted to receive user inputs such a, but not limited to from epilepsy disease symptoms, medical history, lifestyle factors, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the user inputs including known, related art, and/or later developed technologies.
[0033] The user may provide input using the computer application 104 installed on the input unit 102. The computer application 104 may comprise the user interface 106. The user interface 106 may enable the user to interact and operate the system 100, in an embodiment of the present invention.
[0034] According to embodiments of the present invention, the input unit 102 may be, but not limited to, a mobile, a tablet, a computer, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the input unit 102, including known, related art, and/or later developed technologies. According to embodiments of the present invention, the computer application 104 may be, but not limited to, an android application, a web application, a standalone application, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the computer application 104 including known, related art, and/or later developed technologies.
[0035] In an embodiment of the present invention, the database 108 may be adapted to store the user inputs received from the input unit 102. The database 108 may further be adapted to store and implement a plurality of machine learning models, in an embodiment of the present invention. In an embodiment of the present invention, the plurality of the machine learning models may be, but not limited to, a Logistic Regression Algorithm, a K-Nearest Neighbor (KNN) Algorithm, a Support Vector Machine (SVM) Algorithm, a Naïve Byes Algorithm, a Decision Tree Algorithm, a Random Forest Algorithm, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the machine learning models including known, related art, and/or later developed technologies.
[0036] In an embodiment of the present invention, the processing unit 110 may be communicating with the input unit 102. The processing unit 110 may further be configured to execute computer-executable instructions to generate an output relating to the system 100. According to embodiments of the present invention, the processing unit 110 may be, but not limited to, a Programmable Logic Control (PLC) unit, a microprocessor, a development board, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the processing unit 110 including known, related art, and/or later developed technologies. In an embodiment of the present invention, the processing unit 110 may further be explained in conjunction with FIG. 2.
[0037] FIG. 1B, FIG. 1C, FIG. 1D and FIG. 1E illustrate the user interface 106 of the system 100, according to the embodiments of the present invention. In an embodiment of the present invention, the user interface 106 of the system 100 may enable the user to provide user inputs using the input. Further, after receipt of the user inputs, the user interface 106 may either display that the user may be affected by the epileptic seizures, or the user may be unaffected by the epileptic seizures. Further, if the user may be affected by the epileptic seizures, then the user interface 106 may display the personalized prediction and precautionary guidance, in an embodiment of the present invention.
[0038] FIG. 2 illustrates a block diagram of the processing unit 110 of the system 100 for the personalized prediction and precautionary guidance for epilepsy disease using machine learning techniques, according to an embodiment of the present invention. The processing unit 110 may comprise the computer-executable instructions in form of programming modules such as a data receiving module 200, a feature extraction module 202, a machine learning module 204, an analysis module 206, a classification module 208, and a prediction module 210.
[0039] In an embodiment of the present invention, the data receiving module 200 may be configured to receive the user inputs from the input unit 102. These user inputs may include various epilepsy disease symptoms, medical history, lifestyle factors, and so forth. In an embodiment of the present invention, the data receiving module 200 may request additional data points from the user, that may be but not limited to, physiological measurements, medication schedules, time charts, and any other relevant information pertaining to the user's condition. In an embodiment of the present invention, the feature extraction module 202 may be configured to extract features from the received user inputs using preprocessing techniques. These features may encompass statistical parameters, frequency domain features, time-domain features, or any other relevant characteristics derived from the input data to be used for subsequent analysis.
[0040] In an embodiment of the present invention, the machine learning module 204 may be configured to apply the plurality of machine learning models based on the extracted features to predict a likelihood of epileptic seizures by each of the plurality of machine learning models. These machine learning models may include algorithms such as support vector machines, random forests, neural networks, or ensemble methods, trained on historical data to make accurate predictions.
[0041] In an embodiment of the present invention, the analysis module 206 may be configured to conduct a comparative analysis of the predicted likelihood of the epileptic seizures by each of the plurality of machine learning models. This analysis could involve evaluating the performance metrics of each model, such as accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC-ROC), to determine the most reliable predictors.
[0042] In an embodiment of the present invention, the classification module 208 may be configured to perform classifier identification to determine a final likelihood of the epileptic seizures. This step may involve combining the predictions from the multiple machine learning models using techniques such as weighted averaging, voting schemes, or model stacking to arrive at a consensus prediction. In an exemplary embodiment of the present invention, by using the weighted averaging, the classification module 208 may assign different weights to the predictions of each of the models based on their performance metrics to ensure that more reliable models contribute more to the final prediction. In another exemplary embodiment of the present invention, by using the voting schemes, the classification module 208 may combine the predictions through a democratic process, where each model gets one vote, and the final prediction is determined by a most common outcome or by considering the confidence levels of each model. In another exemplary embodiment of the present invention, by using the Model stacking, the classification module 208 may employ a meta-model that learns to combine the outputs of base models, leveraging the strengths of each model to improve overall prediction accuracy.
[0043] In an embodiment of the present invention, the prediction module 210 may be configured to generate the personalized prediction and precautionary guidance based on the final likelihood of epileptic seizures. This could involve providing the user with tailored recommendations and precautions based on their individual risk profile, such as medication adjustments, lifestyle modifications, or alerts for potential seizure triggers, aimed at mitigating the risk of epileptic episodes.
[0044] FIG. 3 depicts a flowchart of a method 300 for the personalized prediction and precautionary guidance for epilepsy disease using machine learning techniques using the system 100, according to an embodiment of the present invention.
[0045] At step 302, the system 100 may receive the user inputs from the input unit 102.
[0046] At step 304, the system 100 may extract features from the received user inputs using preprocessing techniques.
[0047] At step 306, the system 100 may apply the plurality of machine learning models based on the extracted features to predict the likelihood of epileptic seizures by each of the plurality of machine learning models.
[0048] At step 308, the system 100 may conduct the comparative analysis of the predicted likelihood of the epileptic seizures by each of the plurality of machine learning models.
[0049] At step 310, the system 100 may perform classifier identification to determine the final likelihood of the epileptic seizures.
[0050] At step 312, the system 100 may generate the personalized prediction and precautionary guidance based on the final likelihood of epileptic seizures.
[0051] While the invention has been described in connection with what is presently considered to be the most practical and various embodiments, it is to be understood that the invention is not to be limited to the disclosed embodiments, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the scope of the appended claims.
[0052] This written description uses examples to disclose the invention, including the best mode, and also to enable any person skilled in the art to practice the invention, including making and using any devices or systems and performing any incorporated methods. The patentable scope of the invention is defined in the claims, and may include other examples that occur to those skilled in the art. Such other examples are intended to be within the scope of the claims if they have structural elements that do not differ from the literal language of the claims, or if they include equivalent structural elements within substantial differences from the literal languages of the claims. , Claims:CLAIMS
We Claim:
1. A system (100) for personalized prediction and precautionary guidance for epilepsy disease using machine learning techniques, comprising:
an input unit (102) adapted to receive user inputs selected from epilepsy disease symptoms, medical history, lifestyle factors, or a combination thereof;
a processing unit (110) in communication with the input unit (102), characterized in that the processing unit (110) is configured to:
receive the user inputs from the input unit (102);
extract features from the received user inputs using preprocessing techniques;
apply a plurality of machine learning models based on the extracted features to predict a likelihood of epileptic seizures by each of the plurality of machine learning models;
conduct a comparative analysis of the predicted likelihood of the epileptic seizures by each of the plurality of machine learning models;
perform classifier identification to determine a final likelihood of the epileptic seizures; and
generate the personalized prediction and precautionary guidance based on the final likelihood of the epileptic seizures.
2. The system (100) as claimed in claim 1, wherein the input unit (102) comprises a computer application (104).
3. The system (100) as claimed in claim 1, wherein the input unit (102) comprises a user interface (106).
4. The system (100) as claimed in claim 1, wherein the plurality of machine learning models are selected from a Logistic Regression Algorithm, a K-Nearest Neighbor (KNN) Algorithm, a Support Vector Machine (SVM) Algorithm, a Naïve Byes Algorithm, a Decision Tree Algorithm, a Random Forest Algorithm, or a combination thereof.
5. The system (100) as claimed in claim 1, wherein the machine learning models are stored and implemented on a database (108).
6. A method (300) for personalized prediction and precautionary guidance for epilepsy disease using machine learning techniques using the system (100), the method (300) is characterized by steps of:
receiving user inputs from an input unit (102);
extracting features from the received user inputs using preprocessing techniques;
applying a plurality of machine learning models based on the extracted features to predict a likelihood of epileptic seizures by each of plurality of machine learning models;
conducting a comparative analysis of the predicted likelihood of the epileptic seizures by each of the plurality of machine learning models;
performing classifier identification to determine a final likelihood of the epileptic seizures; and
generating the personalized prediction and precautionary guidance based on the final likelihood of the epileptic seizures.
7. The method (300) as claimed in claim 6, wherein the machine learning models are selected from a Logistic Regression Algorithm, a K-Nearest Neighbor (KNN) Algorithm, a Support Vector Machine (SVM) Algorithm, a Naïve Byes Algorithm, a Decision Tree Algorithm, a Random Forest Algorithm, or a combination thereof.
Date: May 28, 2024
Place: Noida

Dr. Keerti Gupta
Agent for the Applicant
(IN/PA-1529)