Abstract
Diagnosis of the progressive neurological disorder, i.e., Parkinson’s disease through different machine learning techniques provides better insights from Parkinson’s dataset in the present decennary. Low dopamine levels in the brain are the major medical condition for Parkinson’s disease. According to stats, nine out of ten people with Parkinson’s disease are suffering from a speech disorder. The major goal of this paper is to diagnose the lifetime neural disorder at an early stage for further precautions and medication. Modern techniques like machine learning and deep learning are playing a crucial role in the correct and efficient diagnosis of Parkinson’s disease. In this paper, we have diagnosed Parkinson’s disease with the help of seven classifiers to find out the best and efficient classifier for this disability condition. Two methods were analyzed for tackling the situation in an efficient manner. The first technique uses the dimensionality reduction technique, i.e., principal component analysis, whereas the second one was simple where all the dimensions of the dataset were considered for training and evaluation. Consideration of all the dimensions for evaluation and testing performed very well w. r. t data reduction technique where LightGBM performed excellently by achieving the highest accuracy of 0.9118 with an AUC of 0.9292 in the receiver operating characteristics (ROC) curve for correctly classifying that whether a person is suffering from Parkinson’s disease or not.
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Gourisaria, M.K., Jain, P., Singh, V., Choudhury, T. (2022). A Duplex Method for Classification of Parkinson’s Disease Using Data Reduction Techniques. In: Rout, R.R., Ghosh, S.K., Jana, P.K., Tripathy, A.K., Sahoo, J.P., Li, KC. (eds) Advances in Distributed Computing and Machine Learning. Lecture Notes in Networks and Systems, vol 427. Springer, Singapore. https://doi.org/10.1007/978-981-19-1018-0_48
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DOI: https://doi.org/10.1007/978-981-19-1018-0_48
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