Diagnosis of Parkinson’s Disease with Acoustic Sounds by Rule Based Model

Abstract

Parkinson’s disease causes disruption in many vital functions such as speech, walking, sleeping, and movement, which are the basic functions of a human being. Early diagnosis is very important for the treatment of this disease. In order to diagnose Parkinson’s disease, doctors need brain tomography, and some biochemical and physical tests. In addition, the majority of those suffering from this disease are over 60 years of age, make it difficult to carry out the tests necessary for the diagnosis of the disease. This difficult process of diagnosing Parkinson’s disease triggers new researches. In our study, rule-based diagnosis of parkinson’s disease with the help of acoustic sounds was aimed. For this purpose, 188 (107 Male-81 Female) individuals with Parkinson’s disease and 64 healthy (23 Male-41 Female) individuals were asked to say the letter ‘a’ three times and their measurements were made and recorded. In this study, the data set of recorded 756 measurements was used. Baseline, Time, Vocal, MFCC and Wavelet that are extracted from the voice recording was used. The data set was balanced in terms of the “Patient/Healthy” feature. Then, with the help of Eta correlation coefficient based feature selection algorithm (E-Score), the best 20% feature was selected for each property group. For the machine learning step, the data were divided into two groups as 75% training, 25% test group with the help of systematic sampling method. The accuracy of model performance was evaluated with Sensivity, Specifitiy, F-Measurement, AUC and Kapa values. As a result of the study, it was found that the disease could be detected accurately with an accuracy rate of 84.66% and a sensitivity rate of 0.96. High success rates indicate that patients can be diagnosed with Parkinson’s disease with the help of their voice recordings. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.