A new approach: information gain algorithm-based k-nearest neighbors hybrid diagnostic system for Parkinson's disease

Parkinson's disease (PD) is a slow and insidiously progressive neurological brain disorder. The development of expert systems capable of automatically and highly accurately diagnosing early stages of PD based on speech signals would provide an important contribution to the health sector. For this purpose, the Information Gain Algorithm-based K-Nearest Neighbors (IGKNN) model was developed. This approach was applied to the feature data sets formed using the Tunable Q-factor Wavelet Transform (TQWT) method. First, 12 sub-feature data sets forming the TQWT feature group were analyzed separately after which the one with the best performance was selected, and the IGKNN model was applied to this sub-feature data set. Finally, it was observed that the performance results provided with the IGKNN system for this sub-feature data set were better than those for the complete set of data. According to the results, values of receiver operating characteristic and precision-recall curves exceeded 0.95, and a classification accuracy of almost 98% was obtained with the 22 features selected from this sub-group. In addition, the kappa coefficient was 0.933 and showed a perfect agreement between actual and predicted values. The performance of the IGKNN system was also compared with results from other studies in the literature in which the same data were used, and the approach proposed in this study far outperformed any approaches reported in the literature. Also, as in this IGKNN approach, an expert system that can diagnose PD and achieve maximum performance with fewer features from the audio signals has not been previously encountered.

Automated summary

The IGKNN model developed in this study showed excellent performance in diagnosing early stages of PD based on speech signals, outperforming other approaches reported in the literature.