Prediction of Parkinson’s Disease Using Machine Learning Models—A Classifier Analysis

Abstract

Among the chronic nervous system diseases, Parkinson's disease (PD) is known for its progressiveness in impairing the speech ability, gait as well as complex muscle and nerve actions. Hence an early diagnosis of PD will help in reducing the symptoms. Telemedicine offers a cost-effective and convenient approach, and several studies have used dysphonic features to remotely detect PD. In this study, we have used a data set from Kaggle, which included voice measurements from 31 people of whom 23 were diagnosed with PD. The data set included 22 different attributes pertaining to voice measurements, including the pitch period entropy with 195 voice recordings for each of the individuals. In the data pre-processing, the correlated attributes were removed and we used 10 non-correlating attributes (< 0.7) along with individual status (0 and 1 for healthy and PD, respectively). The data set after preprocessing was split into 70:30 ratio and also ascertained that the number normal versus PD are in equal ratios in both the training and testing data sets, respectively. The data set was evaluated with four different supervised classification machine learning (ML) models, namely random forest, XGBoost, SVM and decision tree. The XGBoost classifier model was found to be highly efficient in precise classification of PD with an accuracy of 0.93.

Severity Prediction over Parkinson’s Disease Prediction by Using the Deep Brooke Inception Net Classifier

“…Human vocal signals are then examined to determine the PD patient's voice strength and frequency. In [ 14 ] the author utilised voice measures from 31 participants, 23 of whom had been diagnosed with Parkinson's disease (PD), using a data set obtained from Kaggle. Each person's 195 voice recordings were included in the data collection, which comprised 22 distinct characteristics related to voice measures.…”

Machine Learning Methods for Early-Stage Diagnosis of Parkinson's Disease Through Handwriting Data

“…One way to predict and diagnose a disease based on any of its symptoms and test its accuracy is by conducting data analysis through machine learning on the collected samples for the symptom/s. In PD early detection through voice, gait patterns, and handwriting data, commonly used classification algorithms are support vector machine (SVM), random forest, k-nearest neighbors (KNN), as well as Adaboost classifier [10][11][12][13][14][15][16]. The detection of Parkinson's disease heavily relies on the early stages of diagnosis, given its complexity and clinical challenges that prevent definitive diagnosis that cause difficulty at later stages in managing the symptoms [9,17].…”