A Healthcare Framework for Early Detection and Management of Parkinson and Chronic Diseases Using Advanced Machine Learning Techniques

Abstract

This paper explores the application of various machine learning algorithms and deep neural networks (DNN) for the prediction of chronic diseases, specifically diabetes and Parkinson's disease. The study employs multiple datasets, including the Pima Indian Diabetes Dataset and other publicly available health datasets, to evaluate the performance of models such as Logistic Regression (LR), Random Forest (RF), Gradient Boosting (GB), XGBoost (XGB), LightGBM (LGBM), Multilayer Perceptron (MLP), and a custom DNN. The combined model, integrating both deep learning and traditional machine learning techniques, demonstrates superior performance with high precision and recall values across multiple classes. Confusion matrix analysis further confirms the robustness and reliability of these models in accurately classifying chronic disease cases. The findings underscore the potential of advanced machine learning techniques in improving early detection and management of chronic diseases, ultimately contributing to better patient outcomes and healthcare efficiency.