Personalized Data-Driven Robust Machine Learning Models to Differentiate Parkinson's Disease Patients Using Heterogeneous Risk Factors

Parkinsons Disease (PD) is the most prevalent neurodegenerative disorder after Alzheimers, yet its diagnosis largely relies on subjective clinical assessments. Thus, this study proposes a systematic, data-driven approach to accurately classify PD patients using heterogeneous risk factors along with efficient machine learning. Six machine learning algorithms, Support Vector Machine(SVM), Random Forest(RF), Extreme Gradient Boosting(XGBoost), Logistic Regression(LR), K-Nearest Neighbour (KNN), and Decision Tree(DT) were utilized and evaluated their performances to identify the most robust and efficient model with high discrimination power. SVM model outper-formed all other machine learning models, and it has been identified as the highest-quality model to classify PD patients from others with at least 96% accuracy. Further-more, Feature importance was analyzed using SHAP to enhance the interpretability of the proposed model. This study contributes to the integration of artificial intelligence in the healthcare domain, emphasizing the value of data-driven classification modeling techniques in supporting healthcare professionals with accurate, personalized, and actionable insights for high-risk patients. Together, these approaches enhance the precision of early detection of PD, paving the way for more informed clinical decision-making and improved patient care.