Parkinson’s Disease Classification Using Random Forest Kerb Feature Selection

作     者：E.Bharath T.Rajagopalan 

作者机构：Department of Computer Science and EngineeringUniversity College of EngineeringVillupuramKakupppam605103India Department of Science and HumanitiesUniversity of College of EngineeringAriyalurKavanur621705India 

出 版 物：《Intelligent Automation & Soft Computing》 (智能自动化与软计算（英文）)

年 卷 期：2023年第36卷第5期

页      面：1417-1433页

学科分类：1002[医学-临床医学] 10[医学] 

主　　题：Parkinson’s disease machine learning healthcare random forest feature selection classification 

摘      要：Parkinson’s disease(PD)is a neurodegenerative disease cause by a deficiency of *** have identified the voice as the underlying symptom of *** vocal disorder studies provide adequate treatment and support for accurate PD *** learning(ML)models have recently helped to solve problems in the classification of chronic *** work aims to analyze the effect of selecting features on ML efficiency on a voice-based PD detection *** includes PD classification models of Random forest,decision Tree,neural network,logistic regression and support vector *** feature selection is made by RF mean-decrease in accuracy and mean-decrease in Gini *** forest kerb feature selection(RFKFS)selects only 17 features from 754 *** proposed technique uses validation metrics to assess the performance of ML *** results of the RF model with feature selection performed well among all other models with high accuracy score of 96.56%and a precision of 88.02%,a sensitivity of 98.26%,a specificity of 96.06%.The respective validation score has an Non polynomial vector(NPV)of 99.47%,a Geometric Mean(GM)of 97.15%,a Youden’s index(YI)of 94.32%,and a Matthews’s correlation method(MCC)90.84%.The proposed model is also more robust than other *** was also realised that using the RFKFS approach in the PD results in an effective and high-performing medical classifier.