Fast and robust supervised machine learning approach for classification and prediction of Parkinson’s disease onset
Lavanya Madhuri Bollipo, K. V. Kadambari
Abstract
Parkinson’s disease (PD) is an incurable long-term neurodegenerative disorder that mainly influence the motor system and eventually results in significant morbidity. The use of computational tools for classification and prediction of PD is desirable for detecting the symptoms at the onset. In this paper, we developed a novel machine learning technique based on incremental support vector machine and modified Frank-Wolfe method (SVM-MFW) for classification and prediction of PD onset. The proposed model works on heterogeneous data gathered from the Parkinson’s progression markers initiative (PPMI) data repository. The work when compared to earlier state-of-the-art techniques outplays them in terms of both classification and prediction. It is shown that the model achieves an equivalent accuracy of 98.3% and a low cross-entropy value in less computational time.