Abstract
Parkinson’s disease (PD) is one of the illnesses which influences the development and its moderate and non-treatable sensory system problem. Side effects of Parkinson’s infection might incorporate quakes, inflexible muscles, stance and equilibrium weakness, discourse changes, composing changes, decline squinting, grinning, arms development, and so on. The manifestations of Parkinson’s illness deteriorate as time elapses by. The early location of Parkinson’s sickness is one of the critical applications in the present time because of this explanation. According to the execution, part is concerned it is partitioned into two unique parts. The first incorporates pre-handling of the MRI picture dataset utilizing different methods like resizing, standardization, histogram coordinating, thresholding, separating, eliminating predisposition and so forth to zero in on the part which is significant and gets more precise outcomes. In the subsequent section, a dataset with different various elements of human discourse which helps in identifying Parkinson’s illness has been utilized. Here additionally, the dataset will be handled first, imagined, adjusted, and afterward, at last, be split into preparing and testing. Utilizing machine learning calculations, we will prepare the model like decision tree classifier, logistic regression, support vector machine, XGBoost and K neighbors classification, and after testing, we will get results utilizing execution boundaries like exactness score, accuracy review, disarray grid and so on.
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Desai, S., Mehta, D., Dulera, V., Chhikaniwala, H. (2022). Parkinson’s Disease Detection Using Machine Learning. In: Raj, J.S., Shi, Y., Pelusi, D., Balas, V.E. (eds) Intelligent Sustainable Systems. Lecture Notes in Networks and Systems, vol 458. Springer, Singapore. https://doi.org/10.1007/978-981-19-2894-9_4
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DOI: https://doi.org/10.1007/978-981-19-2894-9_4
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