WITHDRAWN: A benchmarking study of Parkinson's disease classification base on speech symptom features

Abstract

In numerous prominent fields, such as e-business, communications, retail, and medical, information exploration in databases has built its success rate. The mining of medical data has immense potential to investigate the out-of-sight trends in the respective medical databases. This paper aims to provide a benchmarking analysis of the classification basis for Parkinson's disease (PD) on speech symptoms utilizing a data mining approach. The Mentation, Tasks of Everyday Life (ADL), Motor Analysis, and Therapy Symptoms will captivate various PD main signs. The speech symptom that is an ADL is a frequent cause for the disease's advancement. The dataset PD classification is accessed from UCI, an open archive of broad datasets of machine learning (ML). In this paper, a comparative review is carried out on various classification algorithms by adding the feature significance analysis and the accuracy analysis to the best classification algorithm. Subsequently, a collection of ML algorithms to classify PD are Averaged Perceptron (AP), Bayes Point Ma-chine (BPM), Boosted Decision Trees (BDT), Convolutional Neural Network (CNN), Decision Forest (DF), Logistic Regression (LR), Neural Network (NN), and Support Vector Machine (SVM). Each algorithm is tested and evaluated via a 5-fold cross-validation approach. The results show that the BDT achieves the highest accuracy of 90.03%, precision of 93.81%, recall of 93.32%, and AUC of 95.67. It outperforms all the other seven tested ML classifiers; nevertheless, the other classifiers also manage to produce high PD diagnosis results, which indicates the suitability of the speech analysis approach in PD diagnosis.