Abstract
Parkinson’s disease (PD) is a common chronic neurodegenerative illness characterised by continuous nervous system degradation. This condition is more prevalent in the elderly. In Parkinson’s, dopaminergic neurons die at an early stage, resulting in a progressive neurodegenerative condition. PD can cause a various symptom of non-motor and motor, including smell and speech. One of the problems that patients with Parkinson’s may face is a pronunciation or having difficulty while speaking. As a result, early diagnosis is critical in minimising the potential effects of disease-related speech disorders. This journal intends to build a categorisation scheme for Parkinson’s disease to distinguish between healthy individuals and PD sufferers and create a hybrid classifier by combining distinct machine learning models. For this journal, we have implemented Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Random Forest classifier, and Logistic Regression ML techniques and acquired the classification report. The results showed that Random Forest has outperformed other ML techniques with 89.47% accuracy for the testing set.
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Saxena, R., Andrew, J. (2024). Parkinson’s Disease Identification from Speech Signals Using Machine Learning Models. In: Sharma, H., Chakravorty, A., Hussain, S., Kumari, R. (eds) Artificial Intelligence: Theory and Applications. AITA 2023. Lecture Notes in Networks and Systems, vol 844. Springer, Singapore. https://doi.org/10.1007/978-981-99-8479-4_15
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