Developing a Model for Parkinson’s Disease Detection Using Machine Learning Algorithms  

作　　者：Naif Al Mudawi 

机构地区：[1]Department of Computer Science,College of Computer Science and Information System,Najran University,Najran,55461,Saudi Arabia

出　　处：《Computers, Materials & Continua》2024年第6期4945-4962,共18页计算机、材料和连续体（英文）

基　　金：The funding for thisworkwas provided by theResearch Groups Funding Program,Grant Code(NU/GP/SERC/13/30).

摘　　要：Parkinson’s disease(PD)is a chronic neurological condition that progresses over time.People start to have trouble speaking,writing,walking,or performing other basic skills as dopamine-generating neurons in some brain regions are injured or die.The patient’s symptoms become more severe due to the worsening of their signs over time.In this study,we applied state-of-the-art machine learning algorithms to diagnose Parkinson’s disease and identify related risk factors.The research worked on the publicly available dataset on PD,and the dataset consists of a set of significant characteristics of PD.We aim to apply soft computing techniques and provide an effective solution for medical professionals to diagnose PD accurately.This research methodology involves developing a model using a machine learning algorithm.In the model selection,eight different machine learning techniques were adopted:Namely,Random Forest(RF),Decision Tree(DT),Support Vector Machine(SVM),Naïve Bayes(NB),Light Gradient Boosting Machine(LightGBM),K-Nearest Neighbours(KNN),Extreme Gradient Boosting(XGBoost),and Logistic Regression(LR).Subsequently,the concentrated models were validated through 10-fold Cross-Validation and Receiver Operating Characteristic(ROC)—Area Under the Curve(AUC).In addition,GridSearchCV was utilised to measure each algorithm’s best parameter;eventually,the models were trained through the hyperparameter tuning approach.With 98%accuracy,LightGBM had the highest accuracy in this study.RF,KNN,and SVM came in second with 96%accuracy.Furthermore,the performance scores of NB and LR were recorded to be 76%and 83%,respectively.It is to be mentioned that after applying 10-fold cross-validation,the average performance score of LightGBM accounted for 93%.At the same time,the percentage of ROC-AUC appeared at 0.92,which indicates that this LightGBM model reached a satisfactory level.Finally,we extracted meaningful insights and figured out potential gaps on top of PD.By extracting meaningful insights and identifying potential gaps

关 键 词：Light GBM cross-validation ROC-AUC Parkinson’s disease(PD) SVM and XGBoost 

分 类 号：R742.5[医药卫生—神经病学与精神病学] TP181[医药卫生—临床医学]