Abstract
Parkinson’s disease (PD) is a prevalent problem among old people above the age of 60 years. It is a neurological disorder which affects the dopamine-producing neurons, which are situated in a part of the human brain called the substantia nigra. Although the aetiology of PD is still unknown, the symptoms appear gradually with age. As the disease progresses, patients may face difficulty with normal motor functions such as walking. They may also witness change in behaviour, undergo mental health problems which might lead to depression, memory loss and fatigue. This paper deals with application of classification algorithms to bridge the gap between medical sciences and artificial intelligence. The fine-tuned model promised an accuracy of 94% on classifying new, undiagnosed patients with a recall of 100%, diminishing chances of misclassification which were prevalent in other researches.
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Bhanja, M.A., Chaudhary, S., Jatain, A. (2021). Model Selection for Parkinson’s Disease Classification Using Vocal Features. In: Singh Pundir, A.K., Yadav, A., Das, S. (eds) Recent Trends in Communication and Intelligent Systems. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-0167-5_16
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DOI: https://doi.org/10.1007/978-981-16-0167-5_16
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