Parkinson’s Disease Prediction Using Machine Learning.

Information Science And Engineering

Parkinson’s disease (PD) is a neurodegenerative disorder that affects millions of people worldwide, causing tremors, stiffness, and difficulty with movement. Early diagnosis and intervention are crucial for managing PD effectively and improving patients' quality of life. In recent years, machine learning (ML) algorithms have shown promise in assisting with the early detection and prediction of PD based on various clinical and biomarker data. This study aims to explore the effectiveness of ML techniques in predicting Parkinson’s disease using relevant features extracted from patient data. A comprehensive dataset comprising demographic information, clinical assessments, and possibly biomarkers is collected from individuals with and without PD. Various ML algorithms, including but not limited to logistic regression, support vector machines, decision trees, random forests, and neural networks, are employed to build predictive models. Feature selection techniques and cross-validation are applied to optimize model performance and generalizability. The performance of each model is evaluated using metrics such as accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC-ROC). Additionally, feature importance analysis is conducted to identify the most discriminative features for PD prediction. The proposed ML models are compared with traditional diagnostic methods to assess their potential clinical utility in early PD detection. The results of this study will contribute to the growing body of research on leveraging ML for the prediction and early diagnosis of Parkinson’s disease. By developing accurate and efficient prediction models, healthcare professionals can potentially identify individuals at risk of developing PD at an earlier stage, enabling timely interventions and personalized treatment strategies. Moreover, the insights gained from this research may pave the way for the development of user-friendly and cost-effective diagnostic tools for Parkinson’s disease prediction in clinical practice.

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