(untitled) (INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY, 2016)
Computer-aided diagnosis (CAD) can be used as a decision support system by physicians in the diagnosis and treatment of disordered speech especially those who specialize in neurophysiology diseases. Parkinson’s disease (PD) is a progressive disorder of the nervous system that affects movement. It develops gradually, sometimes starting with a barely noticeable tremor in speech. It has been found that 80% of persons with PD reported speech and voice disorders. Parkinson’s disease symptoms worsen as the condition progresses over time. Therefore, Speech may become soft or slurred and these deficits in speech intelligibility impact on health status and quality of life. Different researchers are currently working in the analysis of speech signal of people with PD, including the study of different dimensions in speech such as phonation, articulation, prosody, and intelligibility. Here, we present the characteristics and features of normal speech and speech disorders in people with PD and the types of classification for implementation of the efficacy of treatment interventions. The results show that our classification algorithm using ANN is outperformed KNN and SVM. ANN is a practical and useful as a predictive tool for PD screening with a high degree of accuracy, approximately 96.1% of a correct detection rate (sensitivity 94.7%, and specificity 96.6%). Based on the high levels of accuracy obtained by our proposed algorithm, it can be used for enhancing the detection purpose to discriminate PD patients from healthy people. Our algorithm may be used by the clinicians as a tool to confirm their diagnosis.