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Enhancing Parkinson’s Disease Diagnosis Accuracy Through speech signal Algorithm Modeling

Computers, Materials & Continua 2022年第2期70卷 2953-2969页

作者： Omar M.El-Habbak Abdelrahman M.Abdelalim Nour H.Mohamed Habiba M.Abd-Elaty Mostafa A.Hammouda Yasmeen Y.Mohamed Mohanad A.Taifor Ali W.Mohamed School of Information Technology and Computer Science Nile UniversityGiza12677Egypt Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Wireless Intelligent Networks Center(WINC) School of Engineering and Applied SciencesNile UniversityGiza12677Egypt

Parkinson’s disease(PD),one of whose symptoms is dysphonia,is a prevalent neurodegenerative *** use of outdated diagnosis techniques,which yield inaccurate and unreliable results,continues to represent an obstacle in early-stage detection and diagnosis for clinical professionals in the medical *** solve this issue,the study proposes using machine learning and deep learning models to analyze processed speech signals of patients’voice *** of these processed speech signals were obtained and experimented on by random forest and logistic regression *** were highly successful,with 90%accuracy produced by the random forest classifier and 81.5%by the logistic regression ***,a deep neural network was implemented to investigate if such variation in method could add to the *** proved to be effective,as the neural network yielded an accuracy of nearly 92%.Such results suggest that it is possible to accurately diagnose early-stage PD through merely testing patients’*** research calls for a revolutionary diagnostic approach in decision support systems,and is the first step in a market-wide implementation of healthcare software dedicated to the aid of clinicians in early diagnosis of PD.

关键词： Early diagnosis logistic regression neural network Parkinson’s disease random forest speech signal processing algorithms

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