Design and Implementation of ML Model for Early Diagnosis of Parkinson’s Disease using Gait Data Analysis in IoT Environment

Abstract

Parkinson’s disease (PD) is the world’s second most neurodegenerative disorder that results in a steady loss of movement. The symptoms in patients occur slowly with the passage of time are and very hard to identify in its initial stage. So, early diagnosis of PD is the foremost need for timely treatment to people. The introduction of smart technologies like the Internet of Things (IoT) and wearable sensors in the healthcare domain offers a smart way of identifying the symptoms of PD patients. In which smart sensors are worn on the patient’s body which continuously monitor the symptoms in patients and track their possible health status. The major objective of this work is to propose a machine learning-based healthcare model that best classifies the subjects into healthy and Parkinson's patients by extracting the most important features. A step regression-based feature selection method is followed to improve the classification of PD. A Shapiro Wilk test is adopted to check the normality of the gait dataset. This model is implemented on three publicly available Parkinson’s datasets collected from three different studies available on Psyionet. All these data sets contain VGRF recordings obtained from eight different sensors placed under each foot. Experimentation is done on the Jupyter notebook by utilizing Python as a programming language. Experimental results revealed that our proposed model with effective pre-processing, feature extraction, and feature selection method achieved the highest accuracy result of 95.54%, 98.80%, and 94.52% respectively when applied to three datasets. Our research inducts knowledge about significant characteristics of a patient suffering from PD and may help to diagnose and cure at an early stage.