Abstract
The manifestations of Parkinson’s disease (PD) are multifold. One of the manifestations is via the motor movements of a person. With a lot of data available from the Parkinson’s Progressive Markers Initiative (PPMI), the data can be analyzed and predictive analysis can be done. The goal of the effort is to identify the dominant features along with the preeminent machine learning (ML) algorithm that can determine whether a person has Parkinson’s disease or not. In this work, we have used the gait data of a person for motor movement along with the patient’s diagnostic information. Feature selection analysis using correlation coefficient is done on the gait data to provide improved accuracy. Further, those features are applied to the ML algorithms. Our experimental results show LightGBM and Xgboost provided the best result with an accuracy of 91.83%, followed by Extra tree classifier and Logistic Regression with an accuracy of 90.81%.
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Saravanan, C., Samantaray, A., Alex, J.S.R. (2023). Multimodal Analysis of Parkinson’s Disease Using Machine Learning Algorithms. In: Abraham, A., Pllana, S., Casalino, G., Ma, K., Bajaj, A. (eds) Intelligent Systems Design and Applications. ISDA 2022. Lecture Notes in Networks and Systems, vol 716. Springer, Cham. https://doi.org/10.1007/978-3-031-35501-1_47
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DOI: https://doi.org/10.1007/978-3-031-35501-1_47
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