Design of a Predictor Model for Feature Selection using Machine Learning Approaches

Abstract

Parkinson’s disease is a neural degenerative disease where patients’ faces various critical neurological disorders. Thus, the earlier prediction of PD helps to enhance the patients’ life. The prediction of PD in earlier stage is complex and it consumes huge time. Therefore, effectual and appropriate prediction of PD is measured to a challenging factor for the health care experts and practitioners. To deal with this issue and to accurately predict the PD in earlier stage, this work concentrates on machine learning approaches for designing a predictor system. For developing the anticipated model, L1-norm based Genetic algorithm (L1-GA) is applied for predicting PD in the earlier stage. This L1-GA is utilized for selecting the influencing features for accurate prediction. This L1-GA produces newer feature subset from UCI Machine Learning  (ML) dataset for PD for measuring feature weights. For validation , this work considers k-fold cross validation (CV) is used. Also, metrics like accuracy, error rate and execution time are evaluated. The inputs are taken from The PD dataset which is available online for preceding the feature selection process. The optimal accuracy attained with these newly selected sub-sets are considered for further computation. The simulation is performed in Python environment and the experimental findings determine that this study recommends that L1-GA provides better contribution towards PD feature selection and to predict PD in earlier stage. In recent times, Clinical Decision Support System (CDSS) plays an essential role for assisting 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 better trade-off in contrast to prevailing approaches.