Classification of Parkinson Disease with Feature Selection using Genetic Algorithm

Abstract

Parkinson’s disease is a complex neurological disorder that affects various neural, behavioural, and physiological systems. To provide optimal treatment and improve patient outcomes, an accurate and early diagnosis is essential. This study explores the use of Artificial Intelligence techniques to diagnose Parkinson’s disease. The study utilizes four machine learning classifiers: Decision Tree, Logistic Regression, Random Forest, and K-Nearest Neighbors, along with a Genetic Algorithm (GA) for feature selection. The study highlights the effectiveness of GA in selecting the most relevant features from a large dataset. Comparative analysis of the classifiers reveals that the Random Forest classifier, combined with Genetic feature selection, performs the best in terms of accuracy, with an accuracy rate of 93.88%. This research contributes to the growing field of machine learning-based diagnostic tools for neurological disorders and provides valuable insights for the development of accurate, powerful, and patient-focused diagnostic tools for Parkinson’s disease.