COMPREHENSIVE MACHINE LEARNING AND DEEP LEARNING APPROACHES FOR PARKINSON'S DISEASE CLASSIFICATION AND SEVERITY ASSESSMENT
Oumaima Majdoubi
oumaima_majdoubi@um5.ac.maMohammed V University in Rabat, National School of Arts and Crafts, Electronic Systems Sensors and Nanobiotechnology (Morocco)
https://orcid.org/0009-0000-2968-7975
Achraf Benba
Mohammed V University in Rabat, National School of Arts and Crafts, Electronic Systems Sensors and Nanobiotechnology (Morocco)
https://orcid.org/0000-0001-7939-0790
Ahmed Hammouch
Mohammed V University in Rabat, National School of Arts and Crafts, Electronic Systems Sensors and Nanobiotechnology (Morocco)
https://orcid.org/0009-0005-8691-6662
Abstract
In this study, we aimed to adopt a comprehensive approach to categorize and assess the severity of Parkinson's disease by leveraging techniques from both machine learning and deep learning. We thoroughly evaluated the effectiveness of various models, including XGBoost, Random Forest, Multi-Layer Perceptron (MLP), and Recurrent Neural Network (RNN), utilizing classification metrics. We generated detailed reports to facilitate a comprehensive comparative analysis of these models. Notably, XGBoost demonstrated the highest precision at 97.4%. Additionally, we took a step further by developing a Gated Recurrent Unit (GRU) model with the purpose of combining predictions from alternative models. We assessed its ability to predict the severity of the ailment. To quantify the precision levels of the models in disease classification, we calculated severity percentages. Furthermore, we created a Receiver Operating Characteristic (ROC) curve for the GRU model, simplifying the evaluation of its capability to distinguish among various severity levels. This comprehensive approach contributes to a more accurate and detailed understanding of Parkinson's disease severity assessment.
Keywords:
Parkinson's disease, severity assessment, machine learning, XGBoost, Gated Recurrent Unit (GRU), comparative analysisReferences
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Authors
Oumaima Majdoubioumaima_majdoubi@um5.ac.ma
Mohammed V University in Rabat, National School of Arts and Crafts, Electronic Systems Sensors and Nanobiotechnology Morocco
https://orcid.org/0009-0000-2968-7975
Authors
Achraf BenbaMohammed V University in Rabat, National School of Arts and Crafts, Electronic Systems Sensors and Nanobiotechnology Morocco
https://orcid.org/0000-0001-7939-0790
Authors
Ahmed HammouchMohammed V University in Rabat, National School of Arts and Crafts, Electronic Systems Sensors and Nanobiotechnology Morocco
https://orcid.org/0009-0005-8691-6662
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