METHOD FOR EARLY DETECTION OF PARKINSON'S DISEASE USING MACHINE LEARNING TECHNIQUES

Abstract

The present invention provides a method for the early detection of Parkinson’s disease using advanced machine learning techniques to enhance diagnostic accuracy and reliability. Parkinson’s disease, a progressive neurological disorder, is characterized by motor symptoms such as tremors, rigidity, and bradykinesia. The invention utilizes a multi-step approach, incorporating data preprocessing, feature selection, and the training of multiple machine learning models including Random Forest Classifier, XGBoost, and Xception Architecture. An ensemble learning approach combines the outputs of these models to improve overall prediction accuracy, achieving a train accuracy of 100% and a test accuracy of 97%. The system processes a structured dataset comprising clinical and demographic features and provides real-time diagnostic predictions through a user-friendly software platform. This invention aims to enable early intervention and improve patient outcomes by detecting Parkinson's disease at its earliest stages with high precision.

Specification

Description:[0001] The present invention relates to the field of medical diagnostics and healthcare technology, specifically focusing on the early detection and prediction of neurological disorders, particularly Parkinson's disease. The invention leverages advanced machine learning algorithms, including Random Forest Classifier, XGBoost, and Xception Architecture, to create a non-invasive, efficient, and scalable diagnostic tool that enables healthcare professionals to detect Parkinson's disease at an early stage. By integrating data analytics and computational modeling into medical diagnostics, this invention aims to improve patient outcomes through timely and accurate predictions.

Background of the Invention
[0002] Parkinson's disease is a chronic and progressive neurological disorder that affects the central nervous system, primarily influencing motor functions. It is characterized by symptoms such as tremors, muscle rigidity, bradykinesia (slowness of movement), and postural instability. These symptoms worsen over time, significantly impacting the quality of life of affected individuals.
[0003] Currently, the diagnosis of Parkinson's disease relies heavily on clinical assessments, imaging techniques (e.g., MRI and PET scans), and the observation of motor symptoms. However, these methods may not be effective at detecting the disease in its early stages when symptoms are subtle or non-specific. Moreover, imaging techniques can be expensive and may not be accessible in all healthcare settings, making early diagnosis a challenge, particularly in resource-limited environments.
[0004] As there is no cure for Parkinson's disease, early detection and intervention are critical to managing symptoms and slowing the progression of the disease. Developing an automated and reliable diagnostic method that can predict Parkinson's disease in its early stages using machine learning offers a promising solution. Machine learning algorithms can analyze large datasets of patient information, recognize patterns, and make predictions based on clinical and demographic features. This invention aims to fill the existing gap by providing a system that enhances early diagnosis accuracy and accessibility.

Objects of the Invention
[0005] An object of the present invention is to develop a machine learning-based predictive model capable of accurately identifying individuals at risk of Parkinson's disease at an early stage. This model is designed to be both accurate and efficient, providing real-time diagnosis.
[0006] Another object of the present invention is to utilize Random Forest Classifier, XGBoost, and Xception Architecture for accurate classification of individuals based on medical and demographic features.
[0007] Yet another object of the present invention is to achieve high accuracy in both training and testing phases of the model, demonstrating the reliability and robustness of the proposed approach.
[0008] Another object of the present invention is to employ ensemble learning techniques for improved model performance and precision in diagnosis.
[0009] Another object of the present invention is to offer a non-invasive, cost-effective, and scalable solution for early Parkinson's disease detection.

Summary of the Invention
[0010] The invention presents a method for the early detection of Parkinson's disease through the application of machine learning algorithms. The proposed system processes a structured dataset containing relevant patient information, including clinical symptoms (e.g., tremors, muscle rigidity) and demographic features (e.g., age, gender). The method includes several stages: data preprocessing, feature selection, model training using Random Forest Classifier, XGBoost, and Xception Architecture, and the implementation of an ensemble learning approach to enhance prediction accuracy.
[0011] The data preprocessing step involves normalization, handling missing values, and encoding categorical variables to ensure data consistency and quality. Feature selection techniques identify the most influential attributes, improving the model's efficiency by reducing irrelevant or redundant features. The model is then trained and optimized using hyperparameter tuning to maximize accuracy and performance.
[0012] The ensemble learning approach combines the outputs from the individual models, leveraging their unique strengths to improve overall classification accuracy. This method achieved a training accuracy of 100% and a testing accuracy of 97%, demonstrating its efficacy in distinguishing individuals with Parkinson's disease from healthy subjects.
[0013] The final implementation of the model includes deploying it on a user-friendly software platform, enabling healthcare professionals to input patient data and receive real-time, non-invasive predictions. The system is designed to adapt to new datasets, making it scalable and effective across various healthcare environments.
[0014] In this respect, before explaining at least one object of the invention in detail, it is to be understood that the invention is not limited in its application to the details of set of rules and to the arrangements of the various models set forth in the following description or illustrated in the drawings. The invention is capable of other objects and of being practiced and carried out in various ways, according to the need of that industry. Also, it is to be understood that the phraseology and terminology employed herein are for the purpose of description and should not be regarded as limiting.
[0015] These together with other objects of the invention, along with the various features of novelty which characterize the invention, are pointed out with particularity in the disclosure. For a better understanding of the invention, its operating advantages and the specific objects attained by its uses, reference should be made to the accompanying drawings and descriptive matter in which there are illustrated preferred embodiments of the invention.

Detailed description of the Invention
[0016] An embodiment of this invention, illustrating its features, will now be described in detail. The words "comprising," "having," "containing," and "including," and other forms thereof are intended to be equivalent in meaning and be open-ended in that an item or items following any one of these words is not meant to be an exhaustive listing of such item or items or meant to be limited to only the listed item or items.
[0017] The terms "first," "second," and the like, herein do not denote any order, quantity, or importance, but rather are used to distinguish one element from another, and the terms "a" and "an" herein do not denote a limitation of quantity, but rather denote the presence of at least one of the referenced item.

[0018] The invention is a comprehensive method designed to detect Parkinson's disease at an early stage using advanced machine learning techniques. The method involves a multi-step process that includes data collection, preprocessing, feature selection, model training, ensemble learning, and deployment in a user-friendly software platform. The following is a detailed step-by-step description of each component of the invention:
1. Data Collection and Preprocessing:
[0019] Data Collection: The invention utilizes a structured dataset comprising medical records and clinical information of individuals. This dataset includes various features relevant to Parkinson's disease diagnosis, such as:
[0020] Clinical Symptoms: Tremors, muscle rigidity, bradykinesia (slowness of movement), postural instability, and other motor symptoms associated with Parkinson's disease.
[0021] Demographic Information: Age, gender, lifestyle factors, family history of neurological disorders, and other personal information that could influence disease risk.
[0022] Other Relevant Medical Data: Previous diagnoses, medications, and health history that might provide insight into the individual's condition.
[0023] Data Preprocessing: This step ensures the dataset is clean, consistent, and suitable for machine learning algorithms. It involves several tasks:
[0024] Handling Missing Values: Missing values in the dataset are managed using techniques such as mean/mode imputation or k-nearest neighbors (KNN) imputation to avoid bias and maintain data integrity.
[0025] Normalization: The features in the dataset are normalized to a uniform scale, ensuring that variables with different ranges do not skew the results. Techniques like Min-Max Scaling or Z-score normalization are used to bring all variables to the same range.
[0026] Encoding Categorical Variables: Categorical variables (e.g., gender) are encoded into numerical values using techniques such as one-hot encoding or label encoding, making them suitable for analysis by machine learning algorithms.
2. Feature Selection:
[0027] Identification of Relevant Features: Feature selection is critical for optimizing model performance. The method applies correlation analysis to identify relationships between features and the target variable (Parkinson's disease diagnosis). Features showing strong correlations are prioritized.
[0028] Feature Importance Ranking: Algorithms such as Random Forest or decision trees are used to rank features based on their importance in predicting Parkinson's disease. Features contributing most significantly to the model's predictive power are retained, while irrelevant or redundant features are removed to reduce complexity and improve model efficiency.
[0029] Dimensionality Reduction: In some cases, techniques like Principal Component Analysis (PCA) are applied to reduce the dimensionality of the dataset while retaining its essential information. This step minimizes computational costs and enhances model speed without sacrificing accuracy.
3. Model Training Using Multiple Algorithms:
[0030] Splitting the Dataset: The processed dataset is split into training and testing subsets, typically with an 80:20 ratio. The training set is used to develop the models, while the testing set evaluates their performance.
[0031] Training Models: Three specific machine learning models are employed for training:
[0032] Random Forest Classifier: This model uses multiple decision trees to create a robust classification system. It reduces overfitting and provides stability by averaging the results of multiple trees, ensuring that the model generalizes well to unseen data.
[0033] XGBoost (Extreme Gradient Boosting): XGBoost is selected for its efficiency and predictive power. This algorithm builds decision trees sequentially, optimizing them through gradient boosting. XGBoost is known for handling missing values effectively and offering high accuracy with minimal computational resources. Hyperparameter tuning is applied to optimize aspects like the number of estimators, learning rate, and tree depth.
[0034] Xception Architecture: This deep learning model is integrated to enhance pattern recognition capabilities. Xception is a convolutional neural network (CNN) architecture designed for image and pattern recognition tasks but can be adapted for structured data. It extracts complex patterns from the dataset, improving the model's ability to identify subtle indicators of Parkinson's disease.
[0035] Hyperparameter Tuning: Each model undergoes a hyperparameter tuning process to optimize performance. Techniques like Grid Search or Random Search are used to find the best combination of parameters, such as learning rate, number of trees, or neural network layers, ensuring maximum accuracy and minimal error rates.
4. Ensemble Learning Approach:
[0036] Combining Multiple Models: To leverage the strengths of each algorithm, the invention applies an ensemble learning approach. This technique combines the outputs from the Random Forest Classifier, XGBoost, and Xception models, providing a more accurate and stable prediction than any single model.
[0037] Ensemble Techniques: The method uses techniques like majority voting or weighted averaging to combine the outputs of the models. For instance:
[0038] Majority Voting: Each model outputs a prediction (e.g., presence or absence of Parkinson's disease), and the final decision is based on the majority vote.
[0039] Weighted Averaging: In cases where one model consistently performs better, the method assigns higher weights to its predictions, enhancing the overall accuracy of the ensemble model.
[0040] Evaluation of Ensemble Model: The ensemble approach is evaluated using cross-validation techniques to test its performance and robustness across multiple subsets of the data. This ensures that the model performs well not only on the test set but also on unseen data, minimizing overfitting and maximizing generalization.
5. Model Evaluation and Validation:
[0041] Performance Metrics: The invention evaluates the model using various performance metrics:
[0042] Accuracy: Measures the proportion of correct predictions (both positive and negative) made by the model.
[0043] Precision and Recall: These metrics help assess the model's ability to identify true positives (actual cases of Parkinson's disease) while minimizing false positives (incorrect diagnoses).
[0044] F1 Score: Combines precision and recall into a single metric, providing a balanced evaluation of the model's performance, especially in imbalanced datasets where the number of positive and negative cases differs significantly.
[0045] Evaluation Results: The model achieved a train accuracy of 100% and a test accuracy of 97%, demonstrating its ability to accurately distinguish between individuals with and without Parkinson's disease. These results indicate the model's reliability, robustness, and potential for real-world application.
6. Integration into a Software Platform:
[0046] Development of User Interface: The trained ensemble model is integrated into a user-friendly software platform designed for healthcare professionals. The interface allows users to input patient data (e.g., clinical symptoms, age, gender) and provides real-time predictions based on the ensemble model's analysis.
[0047] Real-Time Prediction and Diagnosis: The system processes the input data and displays a prediction indicating whether the individual is likely to have Parkinson's disease. It also provides a confidence score to indicate the certainty of the prediction, helping healthcare professionals make informed decisions.
[0048] Scalability and Adaptability: The software is designed to be scalable, enabling its deployment in various healthcare settings, from small clinics to large hospitals. The system can also be updated with new data to retrain and fine-tune the model, ensuring it remains accurate as more information becomes available.
[0049] Data Security and Compliance: To ensure patient privacy and compliance with healthcare regulations, the software platform incorporates encryption and secure storage solutions. This guarantees that sensitive patient data is protected during processing and storage, aligning with standards such as HIPAA (Health Insurance Portability and Accountability Act).
7. Deployment and Continuous Improvement:
[0050] Implementation in Healthcare Settings: The invention is deployed in healthcare facilities, providing medical professionals with a reliable tool for early detection of Parkinson's disease. The software is integrated with existing medical databases and electronic health records (EHR) systems to facilitate data input and retrieval, enhancing the workflow efficiency for practitioners.
[0051] Continuous Monitoring and Model Updates: The system is built to continuously collect feedback and new data. As more cases are diagnosed and recorded, the software updates the model to improve its accuracy further. The retraining process ensures that the model adapts to evolving diagnostic criteria or new symptoms identified in Parkinson's disease research.
[0052] Integration with Telemedicine and Remote Monitoring: The invention supports remote patient monitoring by integrating with wearable devices that collect real-time data on motor symptoms (e.g., tremor frequency, movement speed). This integration enables telemedicine applications where healthcare providers can assess and diagnose patients remotely, expanding the reach of Parkinson's disease detection to underserved or rural areas.
[0053] By following this multi-stage process, the invention delivers a comprehensive and efficient method for early detection of Parkinson's disease. This method not only improves diagnostic accuracy but also enhances accessibility and scalability, providing a practical tool for healthcare systems worldwide. The integration of ensemble machine learning models ensures that the system remains robust, reliable, and capable of adapting to new developments in medical diagnostics.
[0054] The foregoing descriptions of specific embodiments of the present invention have been presented for purposes of illustration and description. They are not intended to be exhaustive or to limit the present invention to the precise forms disclosed, and obviously many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described to best explain the principles of the present invention, and its practical application to thereby enable others skilled in the art to best utilize the present invention and various embodiments with various modifications as are suited to the particular use contemplated. It is understood that various omission and substitutions of equivalents are contemplated as circumstance may suggest or render expedient, but such are intended to cover the application or implementation without departing from the spirit or scope of the claims of the present invention.

, Claims:1. A method for detecting Parkinson's disease using machine learning techniques, comprising the steps of:
a) collecting a structured dataset of clinical and demographic features including motor symptoms, age, gender, and medical history;
b) preprocessing the dataset by handling missing values, normalizing features, and encoding categorical variables;
c) selecting relevant features based on correlation analysis and ranking using decision tree algorithms;
d) training multiple machine learning models, including Random Forest Classifier, XGBoost, and Xception Architecture, on the processed dataset;
e) combining the outputs of the models using an ensemble learning approach with majority voting or weighted averaging;
f) evaluating the ensemble model using cross-validation and performance metrics such as accuracy, precision, recall, and F1 score; and
g) deploying the model in a software platform to provide real-time diagnostic predictions and confidence scores for Parkinson's disease detection.

2. The method as claimed in claim 1, wherein the dataset is split into training and testing subsets, with an 80:20 ratio to train the machine learning models and evaluate their performance.

3. The method as claimed in claim 1, wherein the preprocessing step includes using k-nearest neighbors (KNN) imputation to handle missing values, ensuring data integrity.

4. The method as claimed in claim 1, wherein the feature selection process uses Principal Component Analysis (PCA) to reduce dimensionality, minimizing computational cost while retaining essential information.

5. The method as claimed in claim 1, wherein the hyperparameter tuning is applied to the machine learning models using Grid Search or Random Search to optimize parameters, including learning rate, number of estimators, and tree depth.

6. The method as claimed in claim 1, wherein the ensemble learning approach combines the outputs of the machine learning models using weighted averaging to prioritize models with higher predictive accuracy.



7. A software platform for detecting Parkinson's disease, comprising:
an interface for inputting patient data including clinical symptoms, demographic information, and medical history;
a backend system configured to preprocess the input data, perform feature selection, and apply trained machine learning models to provide a diagnostic prediction;
a module to calculate and display confidence scores for each prediction; and
a system to securely store patient data while complying with healthcare privacy regulations.

8. The software platform as claimed in claim 1, wherein the backend system includes encryption protocols to protect patient information during data processing and storage.

9. The software platform as claimed in claim 1, wherein the interface allows integration with electronic health records (EHR) systems, enabling automatic input and retrieval of patient data.

10. The software platform as claimed in claim 1, wherein the system supports remote patient monitoring through integration with wearable devices that collect real-time motor symptom data for remote diagnosis and telemedicine applications.

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