Spearman and Jaccard-Based Convolutional Deep Neural Learning for Early Parkinson’s Diagnosis

Abstract

Parkinson’s disease (PD) is a chronic neurological condition causing an assortment of motor and cognitive prodromes. Each individual’s PD symptoms develop differently due to the variability of the ailment. This study aims to introduce the KNN Imputed Spearman’s Rank and Jaccard Convolutional Deep Neural Learning (KISRJCDNL) technique for automating early PD diagnosis depending on speech analysis. This work enhances disease diagnosis performance through preprocessing and early, precise PD detection. Several information collected from the given dataset are initially taken as input. Then, the preprocessing stage converts raw data into a structured format. Afterward, Spearman’s Rank Feature Selective and Jaccard Index–based Convolutional Deep Neural Learning Classifier with four layers, one input layer, one output layer, and two hidden layers, are deployed for diagnosing PD by efficiently performing the data classification. Experimental evaluation uses the Early Biomarkers of the PD dataset by different factors. Findings support the claim that the proposed KISRJCDNL technique enhances accuracy by 14%, reducing feature selection time, error rate, overall time, and space complexity by 16%, 43%, 36%, and 22% compared to the existing deep learning methods.