A New Metaheuristic Approach to Diagnosis of Parkinson’s Disease Through Audio Signals

Abstract

Parkinson’s disease is accepted as one of the most important diseases in the world. Parkinson’s disease can be diagnosed in various conventional techniques. Recently, these techniques have been replaced by artificial intelligence systems. This study proposes a feature selection and classification technique for Parkinson’s disease based on speech signals using a meta-heuristic algorithm. The proposed method selects the features from the data set including speech signal data that most accurately represent the problem using the efficient search strategies of the immune plasma algorithm (IPA). The experimental results are promising compared to other competing methods for diagnosing Parkinson’s disease in the literature.