Predicting Parkinson’s Disease: An Explainable AI approach with Feature Analysis

Parkinson’s disease (PD) is a degenerative neurological condition that significantly lowers a person’s standard of living. Symptom management and better patient outcomes depend on early diagnosis and intervention. To forecast Parkinson’s disease, this study looks into the use of various ML models, such as Decision Trees, XGBoost, AdaBoost etc. To improve model interpretability, the study highlights the significance of Explainable Artificial Intelligence (XAI) approaches. The essential elements that had a substantial impact on the models’ predictions were found by using feature importance analysis. In addition to illuminating the disease’s fundamental causes, this study offers medical professionals’ practical insights to direct clinical judgment. The integration of machine learning with XAI techniques addresses the critical need for transparency in AI applications within healthcare, ensuring that practitioners can trust and understand the tools they use. Among the classifiers evaluated, the AdaBoost classifier demonstrated exceptional performance, achieving the highest accuracy of 98.34%. This result highlights the strong performance of the AdaBoost algorithm in accurately predicting Parkinson’s disease, demonstrating its suitability and effectiveness for this application. These algorithms are computationally less expensive than the deep learning algorithms and hence could be efficiently used for real-time systems. Also in this paper the contribution of features have been mapped to the accuracy, which shows the explainability of features. The work aims to set the stage for future developments in neurodegenerative disease predictive modeling through thorough analysis and validation.