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Enhancing Parkinson’s Disease Prediction Using Machine Learning and Feature Selection Methods

Computers, Materials & Continua 2022年第6期71卷 5639-5657页

作者： Faisal Saeed Mohammad Al-Sarem Muhannad Al-Mohaimeed Abdelhamid Emara Wadii Boulila mohammed alasli Fahad Ghabban College of Computer Science and Engineering Taibah UniversityMedina41477Saudi Arabia School of Computing and Digital Technology Birmingham City UniversityBirminghamB47XGUnited Kingdom Information System Department Saba’a Region UniversityMareebYemen Computers and Systems Engineering Department Al-Azhar UniversityCairo11884Egypt RIADI Laboratory National School of Computer SciencesUniversity of ManoubaManouba2010Tunisia

Several millions of people suffer from Parkinson’s disease ***’s affects about 1%of people over 60 and its symptoms increase with *** voice may be affected and patients experience abnormalities in speech that might not be noticed by listeners,but which could be analyzed using recorded speech *** the huge advancements of technology,the medical data has increased dramatically,and therefore,there is a need to apply data mining and machine learning methods to extract new knowledge from this *** classification methods were used to analyze medical data sets and diagnostic problems,such as Parkinson’s Disease(PD).In addition,to improve the performance of classification,feature selection methods have been extensively used in many *** paper aims to propose a comprehensive approach to enhance the prediction of PD using several machine learning methods with different feature selection methods such as filter-based and *** dataset includes 240 recodes with 46 acoustic features extracted from3 voice recording replications for 80 *** experimental results showed improvements when wrapper-based features selection method was used with K-NN classifier with accuracy of 88.33%.The best obtained results were compared with other studies and it was found that this study provides comparable and superior results.

关键词： Filter-based feature selection methods machine learning parkinson’s disease wrapper-based feature selection methods

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