Abstract
Detection of Parkinson’s disease is often established on clinical monitoring and evaluation of medical signs, which include an indication of different motor signs. Furthermore, the ordinary point of view encounters rationality as it is based on analysis of motions that are specifically not recognized, tough to categorize, and steers miscategorization. Since Parkinson’s disease sufferers have distinct voice characteristics, vocal recordings are needed and highly regarded as a diagnostic tool. If Machine Learning techniques can truly note this disease with the help of a voice recording dataset, this would be a useful screening phase before seeing a doctor. This chapter shows comparison of some ML models like XGboost, random forest, Naive Bayes, logistic regression, SVM, decision tree, and K-NN to get the best result for detection of disease.
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Bhanuse, V., Chirame, A., Beri, I. (2024). Parkinson’s Disease Detection: Comparative Study Using Different Machine Learning Algorithms. In: Malhotra, R., Sumalatha, L., Yassin, S.M.W., Patgiri, R., Muppalaneni, N.B. (eds) High Performance Computing, Smart Devices and Networks. CHSN 2022. Lecture Notes in Electrical Engineering, vol 1087. Springer, Singapore. https://doi.org/10.1007/978-981-99-6690-5_32
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DOI: https://doi.org/10.1007/978-981-99-6690-5_32
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