Hypokinetic Rigid Syndrome Prognosis using Random Forest Classifiers and Support Vector Machines
Keywords:
Hypokinetic rigid syndrome, Random Forest Classifiers, Support Vector Machines, PrognosisAbstract
Many individuals worldwide experience Hypokinetic Rigid Syndrome (HRS), a condition more prevalent among those aged 50 and above. Despite numerous technological advancements and breakthroughs, early disease diagnosis is still a formidable challenge. This underscores the need for the development of automatic machine learning techniques to aid healthcare professionals in precisely identifying this condition during its initial stages. The primary aim of this research paper is to perform a comprehensive analysis and comparison of contemporary machine learning methods like Support Vector Machine (SVM) and Random Forest (RFM) used for detecting HRS. To assess and determine the most effective and accurate classifier for HRS categorization, this study concentrates on evaluating SVM and RFM on UCI Machine Learning Repository's Parkinson's Data Set. The results indicate that the support vector machine achieved an 84.3% accuracy and a Kappa score of 0.824, while the random forest exhibited an 87.2% accuracy with a Kappa score of 0.82.
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