A Deep Learning-Based Approach to Detect Neurodegenerative Diseases

Abstract

Studies conducted by the World Health Organization (WHO) show that more than a billion people worldwide suffer from neurological disorders and the lack of effective diagnostic procedures affects treatment. Characterizing specific motor symptoms to facilitate their diagnosis can be useful in monitoring disease progression and effective treatment planning. Classification of highly prevalent neurodegenerative diseases (NDD) such as Parkinson’s disease (PH), Amyotrophic Lateral Sclerosis (ALS), and Huntington’s disease (HH) is of clinical importance. One of the methods used in the literature to detect these neurodegenerative diseases is gait analysis-based classification. In this study, 12 different features fed a unidimensional Convolutional Neural Network (CNN) deep learning algorithm-based model, and aims to detect PD,HD, and ALS diseases was studied.The unidimensional CNN deep learning model fed with 12 features achieved 78,92%, 84,39% and 92,09% classification accuracy for control against HH, control against PH, and control detection problems against ALS. Again, the relevant classifier produced 84.75% accuracy with the approach developed to separate all neurodegenerative disease specimens (NDD) under a single label as a class, and to distinguish these diseases against the current control.