Early Parkinson's Disease Detection and Classification using  Machine Learning and Neutrosophic Sets

Hasan H. Oudah; Ahmed A. Metwaly; Mohamed eassa; Ahmed Abdelhafeez; Ahmad M. Nagm; Ahmed S. Salama

Authors

Hasan H. Oudah College of Arts / Department of Journalism, Ahl Al Bayt University, Karbala, Iraq,
Ahmed A. Metwaly Department of Computer Science, Faculty of Computers and Informatics, Zagazig University, Zagazig 44519, Egypt,
Mohamed eassa Computer Science Department, Faculty of Information System and Computer Science, October 6 University, Giza, 12585, Egypt; Applied Science Research Center. Applied Science Private University, Amman, Jordan
Ahmed Abdelhafeez Computer Science Department, Faculty of Information System and Computer Science, October 6 University, Giza, 12585, Egypt; Applied Science Research Center. Applied Science Private University, Amman, Jordan
Ahmad M. Nagm Department of Computer Engineering and Electronics, Cairo Higher Institute for Engineering, Computer Science and Management, New Cairo, Egypt
Ahmed S. Salama Department of Computer Engineering and Electronics, Cairo Higher Institute for Engineering, Computer Science and Management, New Cairo, Egypt

Abstract

Optimizing therapy and rehabilitation for Parkinson's disease (PD) requires early identification
and precise evaluation of the illness's course. However, there is disagreement about the best way
to use gait analysis to categorize the severity of motor symptoms and identify early-stage
Parkinson's disease. The precision of machine learning (ML) models in identifying early and
intermediate stages of Parkinson's disease was assessed in this study. Six ML models are used in
this study for the prediction of PD. Different metrics are used to evaluate ML models such as
accuracy, precision, recall, f1-score, and AUC score. Then we propose a multi-criteria decisionmaking methodology (MCDM) to evaluate the ML models and select the best one. Two MCDM
methods are used such as CRITIC method to compute the criteria weights and the TOPSIS
method to rank the alternatives. These methods are used under the bipolar neutrosophic sets
(BNSs) to deal with uncertainty information. The results show the support vector machine is the
best ML model for the prediction of PD.

DOI: 10.5281/zenodo.15080696