Effective Predictor Model for Parkinson’s Disease Using Machine Learning
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Abstract
Parkinson’s disease is a neurodegenerative disease in which the patient faces various critical neurological disorders. Thus, the earlier prediction of PD helps enhance the patients’ lives. The prediction of PD at an earlier stage is extremely complex and consumes huge amounts of time. Therefore, effective and appropriate prediction of PD is a challenging factor for healthcare experts and practitioners. To deal with this issue and accurately predict the PD at an earlier stage, this work concentrates on machine learning approaches for designing a predictor system. For developing the anticipated model, the L1-norm-based genetic algorithm (L1-GA) is applied for predicting PD at an earlier stage. This L1-GA is utilized for selecting highly related features for accurate classification of Parkinson's disease. This L1-GA produces a newer feature subset from the PD dataset based on its feature weight value.The optimal accuracy attained with these newly selected sub-sets is considered for further computation. The experimental findings determine that this study recommends that L1-GA provides a better contribution toward PD feature selection and can predict PD at an earlier stage. In recent times, the Clinical Decision Support System (CDSS) has played an essential role in assisting with PD recognition. As well, the anticipated model lays a bridge to fill the gap encountered in feature selection using the available data. The anticipated model gives a better trade-off in contrast to prevailing approaches.
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