Description:FIELD OF THE INVENTION
This invention relates to Explainable Neurodegeneration Prediction System Using Harmony Search-Optimized Graph Neural Networks
BACKGROUND OF THE INVENTION
Neurodegenerative illnesses such as Alzheimer's and Parkinson's are fast becoming worldwide health problems, often diagnosed late when damage is already irreversible. Current diagnostic techniques are symptomatic tests and costly imaging tests that are likely to miss symptoms in early stages. Furthermore, AI-based algorithms in current software are "black boxes," and doctors struggle to trust or verify predictions.
This invention solves several important issues:
•Lack of the primary diagnostic ability from disordered and fragmented sources of information.
•Lack of transparency of current AI-based models of diagnosis.
•The fact that complex brain networks cannot be described symbolically using standard algorithms.
•Inability to master responding to new patient data or modified clinical practices.
By the synergistic combination of the structural stability of the Graph Neural Network in identifying brain regions' spatial and functional connectivity and feature extraction with the Harmony Search Algorithm, this invention offers a global and interpretable diagnosis pipeline.
Aside from this, integration of XAI visualization tools can possibly enable clinical experts to make better choices, enhance trust, and minimize false alarms. The end long-term goal is to enhance early diagnosis, minimize treatment expense, and enable the continuous monitoring of patients in a medically acceptable, technically viable, and ethically sound way.
US9963674B2: Provided are age-modified cells and method for making age modified cells using progerin or a progerin-like protein. The aging and/or maturation process can be accelerated and controlled for young and/or immature cells, such as a somatic cell, a stem cell, a stem cell-derived somatic cell, including an induced pluripotent stem cell-derived cell, by contacting with progerin or a progerin-like protein. Methods described by the present disclosure can produce age-appropriate cells from a somatic cell or a stem cell, such as an old cell and/or a mature cell. Such age-modified cells constitute model systems for the study of late-onset diseases and/or disorders.
US20160115444: Provided are age-modified cells and method for making age modified cells using progerin or a progerin-like protein. The aging and/or maturation process can be accelerated and controlled for young and/or immature cells, such as a somatic cell, a stem cell, a stem cell-derived somatic cell, including an induced pluripotent stem cell-derived cell, by contacting with progerin or a progerin-like protein. Methods described by the present disclosure can produce age-appropriate cells from a somatic cell or a stem cell, such as an old cell and/or a mature cell. Such age-modified cells constitute model systems for the study of late-onset diseases and/or disorders.
Neurodegenerative diseases such as Alzheimer’s and Parkinson’s are difficult to diagnose at early stages due to complex brain connectivity patterns, heterogeneous patient data, and lack of transparency in AI-based diagnostic systems. Existing methods rely on black-box deep learning or statistical models that are either computationally expensive, non-interpretable, or unable to handle graph-structured brain data. These limitations lead to delayed detection, low clinician trust, and inefficient clinical decision-making. The present invention solves these problems by combining Graph Neural Networks (GNNs) with the Harmony Search Algorithm (HSA) for optimized feature selection and hyperparameter tuning, while embedding explainable AI (XAI) modules to provide interpretable predictions. This approach ensures accurate, transparent, and scalable neurodegeneration prediction, improving early diagnosis, personalized treatment, and clinical trust.
SUMMARY OF THE INVENTION
This summary is provided to introduce a selection of concepts, in a simplified format, that are further described in the detailed description of the invention.
This summary is neither intended to identify key or essential inventive concepts of the invention and nor is it intended for determining the scope of the invention.
The invention discloses an explainable AI-powered system for neurodegeneration prediction, leveraging Harmony Search-optimized Graph Neural Networks. It integrates multi-modal inputs including neuroimaging, genetic data, and clinical records into graph structures that represent spatial and functional brain connectivity. The Harmony Search Algorithm optimizes both feature subsets and GNN hyperparameters, ensuring robustness and generalizability.
The system incorporates an XAI layer that provides clinicians with interpretable outputs such as visual heatmaps, biomarker importance rankings, and causal feature explanations. These explanations enhance trust in AI predictions by enabling clinicians to verify and understand diagnostic results.
A clinician-friendly dashboard displays prediction scores, disease risk trajectories, and visual evidence of neurodegeneration. The system supports real-time deployment on hospital servers, secure cloud platforms, or edge devices, while ensuring compliance with medical data standards such as DICOM, HL7, and FHIR.
By combining predictive accuracy, interpretability, and adaptability, the invention overcomes the shortcomings of black-box AI models and provides a scalable, clinically viable tool for early neurodegenerative disease detection.
To further clarify advantages and features of the present invention, a more particular description of the invention will be rendered by reference to specific embodiments thereof, which is illustrated in the appended drawings. It is appreciated that these drawings depict only typical embodiments of the invention and are therefore not to be considered limiting of its scope. The invention will be described and explained with additional specificity and detail with the accompanying drawings.
The invention discloses an explainable artificial intelligence (XAI) architecture for neurodegenerative disease prediction, i.e., Alzheimer's and Parkinson's disease. It implements a high-level representation by means of Graph Neural Networks (GNNs) of clinical data and neuroimages with complex patterns along with Harmony Search Algorithm (HSA) for hyperparameter tuning and model feature engineering. The architecture provides explainable decision-making where clinicians are able to view model reasoning and trust prediction. It provides a causative biomarkers and neurodegeneration timeline view interface. It computes multi-modal data such as brain scans, gene expression, and clinical history to provide risk scores and decision explanation. The invention will be of immense assistance in personalized medicine, early treatment, and clinical explanation of diagnosis of neurodegeneration.
BRIEF DESCRIPTION OF THE DRAWINGS
The illustrated embodiments of the subject matter will be understood by reference to the drawings, wherein like parts are designated by like numerals throughout. The following description is intended only by way of example, and simply illustrates certain selected embodiments of devices, systems, and methods that are consistent with the subject matter as claimed herein, wherein:
FIGURE 1: SYSTEM ARCHITECTURE
The figures depict embodiments of the present subject matter for the purposes of illustration only. A person skilled in the art will easily recognize from the following description that alternative embodiments of the structures and methods illustrated herein may be employed without departing from the principles of the disclosure described herein.
DETAILED DESCRIPTION OF THE INVENTION
The detailed description of various exemplary embodiments of the disclosure is described herein with reference to the accompanying drawings. It should be noted that the embodiments are described herein in such details as to clearly communicate the disclosure. However, the amount of details provided herein is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the scope of the present disclosure as defined by the appended claims.
It is also to be understood that various arrangements may be devised that, although not explicitly described or shown herein, embody the principles of the present disclosure. Moreover, all statements herein reciting principles, aspects, and embodiments of the present disclosure, as well as specific examples, are intended to encompass equivalents thereof.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms “a",” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises,” “comprising,” “includes” and/or “including,” when used herein, specify the presence of stated features, integers, steps, operations, elements and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and/or groups thereof.
It should also be noted that in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed concurrently or may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
In addition, the descriptions of "first", "second", “third”, and the like in the present invention are used for the purpose of description only, and are not to be construed as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Thus, features defining "first" and "second" may include at least one of the features, either explicitly or implicitly.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which example embodiments belong. It will be further understood that terms, e.g., those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
Neurodegenerative disorders pose a major global health challenge, with delayed diagnosis contributing to irreversible damage and limited treatment options. The present invention proposes a system that combines advanced AI with explainability and optimization to address these shortcomings.
The system begins with a data acquisition module that ingests multimodal inputs, including MRI, fMRI, PET scans, genetic information, and clinical records. Data preprocessing includes denoising, skull stripping, segmentation, and graph construction, where nodes represent brain regions and edges represent connectivity strengths.
A graph building engine integrates multimodal features into structured brain connectivity graphs. Clinical and genetic attributes may be assigned as node or edge features, enabling richer representations of disease-related patterns.
The invention employs a Harmony Search optimization module to identify the most relevant features and optimize GNN hyperparameters such as learning rate, hidden units, and layer depth. This bio-inspired algorithm efficiently explores high-dimensional search spaces, ensuring optimal model performance.
The Graph Neural Network (GNN) module processes graph-structured data to capture spatial and functional dependencies between brain regions. This enables precise mapping of disease-related interactions that conventional CNNs or statistical models cannot capture.
An explainability layer is integrated to provide transparency in decision-making. Methods such as SHAP, attention mechanisms, or LIME generate interpretable outputs including feature importance graphs, attention maps, and biomarker rankings. This transparency allows clinicians to validate predictions.
A clinician dashboard presents prediction results, explanations, and risk trajectories in an intuitive interface. Predictions may include likelihood of early-stage Alzheimer’s or Parkinson’s, with highlighted brain regions or genetic markers contributing to risk scores.
The system also includes a data storage and feedback loop, enabling continuous model retraining as new patient data becomes available. This ensures adaptability and scalability in clinical practice.
Deployment options include hospital servers for on-site processing, secure cloud APIs for remote access, or lightweight edge devices for portable diagnostic use. Compliance with DICOM protocols ensures integration with imaging hardware, while HL7 and FHIR standards ensure interoperability with electronic health records.
The invention improves diagnostic efficiency by reducing reliance on invasive tests and enhancing trust through explainable AI. It also lowers computational costs via Harmony Search optimization, making it deployable in real-world clinical workflows.
In alternative embodiments, other optimization methods such as Particle Swarm Optimization or Genetic Algorithms can replace Harmony Search. The model is adaptable to other biomedical graph datasets, such as protein-protein interactions.
In another variation, multi-modal data fusion may be extended to wearable sensor signals or speech biomarkers to broaden diagnostic capability. Edge-device implementations allow use in rural or resource-limited settings.
The system provides multiple benefits: improved early diagnosis, reduced healthcare costs, enhanced patient outcomes, and scalable deployment in hospitals or telemedicine platforms.
Overall, the invention represents a clinically explainable, optimized, and adaptive AI system tailored for neurodegenerative disease prediction.
Best Method of Working
The best method of working the invention involves deploying the system on hospital-grade servers with GPU acceleration. Patient brain scans (MRI/fMRI) and genetic/clinical data are ingested through the data acquisition module. Preprocessing creates brain connectivity graphs, which are passed to the Harmony Search module for optimized feature and hyperparameter selection. The GNN processes these graphs to output disease risk scores. The explainability layer generates SHAP-based heatmaps highlighting critical brain regions. A clinician dashboard displays predictions, explanations, and risk timelines. Results are stored securely in a cloud database, enabling longitudinal tracking and model retraining. This configuration maximizes accuracy, interpretability, and clinical utility.
The invention discloses an explainable artificial intelligence (XAI) architecture for neurodegenerative disease prediction, i.e., Alzheimer's and Parkinson's disease. It implements a high-level representation by means of Graph Neural Networks (GNNs) of clinical data and neuroimages with complex patterns along with Harmony Search Algorithm (HSA) for hyperparameter tuning and model feature engineering. The architecture provides explainable decision-making where clinicians are able to view model reasoning and trust prediction. It provides a causative biomarkers and neurodegeneration timeline view interface. It computes multi-modal data such as brain scans, gene expression, and clinical history to provide risk scores and decision explanation. The invention will be of immense assistance in personalized medicine, early treatment, and clinical explanation of diagnosis of neurodegeneration.
Step-by-Step Working Functionality
The system operates step by step as follows:
1.Data Retrieval: Patient data—MRI/fMRI brain images, gene data, and clinical data—are extracted from hospital databases or external laboratories.
2.Data Preprocessing:
Imaging data are processed by operations such as denoising, skull stripping, and segmentation.
Anatomy and function regions are identified and brain connectivity matrices are calculated.
3.Graph Construction:
Preprocessed data are mapped onto a graph whose nodes are brain regions and edges are connection strengths between regions.
Clinical and genetic data may be included as node/edge features.
4.Harmony Search-based Feature Selection:
HSA is used for obtaining the most relevant features (brain areas, markers) and the best GNN parameters.
It decreases the dimensionality and brings generalizability to the model.
5. Graph Neural Network Inference: GNN improves the process with graph data and yields a prediction score of a disease as an estimation of its probability of neurodegeneration.
6. Explainability Layer: XAI layer also informs the prediction of the GNN by indicating which features or image components were most predictive in the prediction.
Visualizations are built in an effort to allow clinicians to understand the decision-making of the model.
7. Clinician Dashboard Output: The previous prediction (e.g., high risk for early-stage Alzheimer's) is displayed with the visual explanations and corresponding data.
8. Data Storage & Feedback: Results are stored securely.
Clinician feedback is employed to retrain and enhance the model on a continuous basis.
This step-by-step approach yields an open, precise, and clinically actionable neurodegeneration prediction model.
Most Important Features
Revelation through invention possesses the following most significant characteristics to develop a meaningful, interpretable model to predict neurodegeneration:
1. Data Acquisition Module: As input accepting neuroimaging data (MRI, fMRI, PET scans), clinical data, gene data, and behavioral test.
2. Preprocessing Unit: Cleans, denoises, and organizes data in the form of brain connectivity graph models.
3. Graph Building Engine: Ties pending multimodal data into structured graph frames with brain regions as nodes and connection relations as edges.
4. Harmony Search Optimization Module: Metaheuristics module for optimizing the optimal feature set and GNN hyperparameters for highest model performance.
5. Graph Neural Network (GNN): Maps spatial and functional patterns of brain data into disease development risk scores.
6. XAI Layer: SHAP or attention maps to produce human-interpretable explanations of model predictions.
7. User Interface Dashboard: Clinical interactive dashboard to see predictions, explanations, and biomarker importance scores.
8. Data Storage and Feedback Loop: Trains prior records and becomes more precise with time by retraining new records.
These all build an explainable, optimized, data-driven early and chronic neurodegenerative disorders detection diagnostic system.
Technology
The innovation is a follow-up of a new trend of personalized hardware and software technologies in medical AI diagnosis:
• Imaging and Sensors Hardware: MRI, fMRI, or PET imaging hardware to scan the brain. Modalities are sensors, capturing structural and functional information of brain regions.
• rewritten Protocols: Complies with DICOM protocols for clinical imaging and HL7/FHIR protocols for EHR interoperability.
Data Preprocessing Methods: Skull stripping, segmentation, and graph building from the connectivity matrices obtained by computing Diffusion Tensor Imaging (DTI) or resting-state fMRI.
•Software Approaches:
• Harmony Search Algorithm (HSA): Bio-inspired optimization algorithm for determining best parameters of GNN and best features.
• Graph Neural Networks (GNNs): Deep learning architecture best capable of dealing with high-complexity graph-structured data, learning between-regional brain interactions.
• Explainable AI Tools: Leverages SHAP (Shapley Additive Explanations), LIME (Local Interpretable Model-Agnostic Explanations), or attention mechanisms for explanation.
•Communication Protocols: Leverages regular TCP/IP, HTTPS for secure cloud-to-cloud communication between edge devices and hospitals.
•Power Supply and Infrastructure: Can be executed on hospital-grade servers or edge AI processors such as NVIDIA Jetson, with support to include GPU acceleration for model inference.
•Programming Frameworks: With Python-support packages pre-installed such as PyTorch Geonic (for GNN deployment), Scikit-learn, TensorFlow, and OpenCV for computer vision.
•Deployment Architecture: Hosted on-premises within healthcare facilities or accessed remotely over a secure cloud API with a healthcare infrastructure-based core.
This bringing together of medical-grade images, sophisticated AI algorithms, and secure computing environments is the system technology core.
ADVANTAGES OF THE INVENTION
• To Environment: Reduces hospitalization, drug prescribing, and energy-intensive imaging tests, lowering healthcare emissions.
• To Society: Enables timely intervention, enhances patients' and carers' and health systems' workload reduction and quality of life.
• To Country: Lowers the cost of chronic care, enhances public health planning, and enhances AI-based innovation for the country's national health system.
Proper Functionality
The algorithm predicted the neurodegenerative disease risk by scanning patients' brain graphs within a Harmony Search-transformed Graph Neural Network. It provides explainable outcomes to doctors, providing the regular brain areas and markers to facilitate prediction. It can build early diagnosis, ongoing learning, and practice amalgamation without high complexity.
This innovation employs Graph Neural Networks and Harmony Search Algorithm solely to improve accuracy and interpretability of neurodegeneration prediction. It differs from other conventional deep learning approaches in the sense that it represents connectivity between brain regions as graph structures, thus facilitating improved spatial and functional interaction modeling. It involves the use of Harmony Search in node and hyperparameter decision making and hence uses robust, lightweight models that could be deployed immediately within a clinic. It is also coupled with inserting XAI modules such that all the predictions yield human-Readable and Explainable information. The new module incorporated in decision-support software designed to work directly in in-reality situations that address doctors included graphically showcasing major factors. The innovation is new in the sense that it delivers predictability performance, interpretability, and efficiency simultaneously, which is hard to achieve all at once through traditional solutions.
 
, Claims:1. A system for explainable prediction of neurodegenerative diseases, comprising:
a) a data acquisition module configured to receive multimodal patient data including neuroimaging, genetic, and clinical records;
b) a preprocessing unit configured to denoise, segment, and construct brain connectivity graphs;
c) a graph building engine for mapping brain regions as nodes and connectivity as edges;
d) a Harmony Search optimization module for selecting optimal features and hyperparameters;
e) a Graph Neural Network module configured to process graph-structured data and generate disease risk scores;
f) an explainability layer employing SHAP, attention mechanisms, or LIME to generate interpretable outputs;
g) a clinician dashboard for visualizing predictions, explanations, and biomarker importance;
h) a data storage and feedback loop for retraining models with new patient data; and
i) a deployment environment operable on cloud, hospital servers, or edge devices.
2. A method for explainable prediction of neurodegenerative diseases using the system as claimed in claim 1, comprising:
a) acquiring multimodal patient data including neuroimaging, genetic, and clinical inputs;
b) preprocessing the data to generate brain connectivity graphs;
c) optimizing features and hyperparameters using the Harmony Search module;
d) applying a Graph Neural Network to infer disease risk scores;
e) generating explainable outputs through the explainability layer;
f) presenting predictions and explanations on a clinician dashboard; and
g) retraining the model with new data via the feedback loop.
3. The system as claimed in claim 1 or the method as claimed in claim 2, wherein the Harmony Search module reduces dimensionality and improves generalizability in high-dimensional datasets.
4. The system as claimed in claim 1 or the method as claimed in claim 2, wherein the Graph Neural Network processes spatial and functional connectivity patterns to detect Alzheimer’s or Parkinson’s disease.
5. The system as claimed in claim 1 or the method as claimed in claim 2, wherein the explainability layer highlights brain regions, genetic markers, or clinical attributes most relevant to predictions.
6. The system as claimed in claim 1 or the method as claimed in claim 2, wherein the clinician dashboard provides risk timelines and biomarker visualization for decision support.
7. The system as claimed in claim 1 or the method as claimed in claim 2, wherein the feedback loop updates model performance continuously with clinician feedback and new patient data.
8. The system as claimed in claim 1 or the method as claimed in claim 2, wherein deployment includes secure interoperability with DICOM imaging protocols and HL7/FHIR EHR standards.
9. The system as claimed in claim 1 or the method as claimed in claim 2, wherein the deployment environment supports hospital servers, secure cloud platforms, or edge devices for portable use.
10. The system as claimed in claim 1 or the method as claimed in claim 2, wherein the system lowers diagnostic costs by optimizing imaging analysis and enhancing interpretability compared to black-box AI models.