Citation: Mian, T.S. An Unsupervised Neural Network Feature Selection and 1D Convolution Neural Network Classification for Screening of Parkinsonism. Diagnostics 2022, 12, 1796. https://doi.org/10.3390/ diagnostics12081796 Academic Editors: Muhammad Fazal Ijaz and Marcin Wo´ zniak Received: 5 June 2022 Accepted: 21 July 2022 Published: 25 July 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2022 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). diagnostics Article An Unsupervised Neural Network Feature Selection and 1D Convolution Neural Network Classification for Screening of Parkinsonism Tariq Saeed Mian Department of IS, College of Computer Science and Engineering, Taibah University, Madinah Al Munawara 43353, Saudi Arabia; tmian@taibahu.edu.sa; Tel.: +966-563304330 Abstract: Parkinson’s disease (PD) is the second most common neurodegenerative disorder after Alzheimer’s disease. It has a slow progressing neurodegenerative disorder rate. PD patients have multiple motor and non-motor symptoms, including vocal impairment, which is one of the main symptoms. The identification of PD based on vocal disorders is at the forefront of research. In this paper, an experimental study is performed on an open source Kaggle PD speech dataset and novel comparative techniques were employed to identify PD. We proposed an unsupervised autoencoder feature selection technique, and passed the compressed features to supervised machine-learning (ML) algorithms. We also investigated the state-of-the-art deep learning 1D convolutional neural network (CNN-1D) for PD classification. In this study, the proposed algorithms are support vector machine, logistic regression, random forest, naïve Bayes, and CNN-1D. The classifier performance is evaluated in terms of accuracy score, precision, recall, and F1 score measure. The proposed 1D-CNN model shows the highest result of 0.927%, and logistic regression shows 0.922% on the benchmark dataset in terms of F1 measure. The major contribution of the proposed approach is that unsupervised neural network feature selection has not previously been investigated in Parkinson’s detection. Clinicians can use these techniques to analyze the symptoms presented by patients and, based on the results of the above algorithms, can diagnose the disease at an early stage, which will allow for improved future treatment and care. Keywords: Parkinson’s disease; ML; linear discriminate analysis; dimensionality reduction; principal component analysis; neural network; random forest; support vector machine; logistic regression 1. Introduction Parkinson’s disease (PD) is a slow progressing neurodegenerative disorder, causing impaired motor function with slow movements, tremors, and gait and balance disturbances. Various non-motor symptoms are common, and include disturbed autonomic function with orthostatic hypotension, constipation and urinary disturbances, sleep disorders, and neuropsychiatric symptoms. The onset of PD is insidious, with peak age of onset being 55–65 years. The disease has drastic effects on the lives of millions of people all over the world [1]. The progression rate of PD is slow; however, as it progresses, the affected person loses control over their movement, resulting in serious issues. The main cause is the loss of dopaminergic neurons in the substantia nigra and the decrease in the level of dopamine in the striatum. PD clinical diagnosis is possible from the following four cardinal motor symptoms: a tremor at rest, bradykinesia, postural instability, and rigidity. The diagnosis of PD based on symptoms becomes possible when almost 60% of dopaminergic neurons are already dead [2]. PD symptoms can vary from person to person due to its variety of symptoms, as exemplified by the multiple types of possible PD tremors, such as limb rigidity, tremors in the hands, and balance and gait problems. PD symptoms are generally classified into two categories: motor (movement-related) and non-motor (unrelated to movement). Diagnostics 2022, 12, 1796. https://doi.org/10.3390/diagnostics12081796 https://www.mdpi.com/journal/diagnostics