JLeNeT: Jaccard LeNet for Parkinson's disease detection and severity level classification using voice signal in IoT environment.

Abstract

The neurodegenerative disorder called Parkinson's disease (PD) is one of the most common diseases now a day. In this research, PD is detected and severity classification is done using the proposed Jaccard LeNet (JLeNet) with the help of voice signal in the IoT environment. Here, the IoT simulation is done. Initially, from which voice signal is collected and the routing process is done by the proposed Chimp Wild Geese Algorithm (ChWGA). This ChWGA is the combination of the Wild Geese Algorithm (WGA) and Chimp Optimization Algorithm (ChOA). Finally, at Base Station (BS), PD is detected and classified. The input voice signal is fed for pre-processing conducted by an adaptive Kalman filter. Following this, feature extraction and feature selection are conducted, where Harmonic mean similarity helps in feature selection. Here, PD is detected using JLeNet, which is the hybridization of LeNet with the Jaccard similarity measure. In this work, routing metrics of energy and delay are superior and recorded with the values of 0.309 J and 0.434 ms for the ChWGA. Moreover, the proposed method attains an Accuracy of 0.910, True Positive Rate (TPR) of 0.903, and True Negative Rate (TNR) of 0.918.