Simple Logistic Hybrid System Based on Greedy Stepwise Algorithm for Feature Analysis to Diagnose Parkinson’s Disease According to Gender

Abstract

The incidence of Parkinson’s disease (PD) is higher in males than in females. This disease can be diagnosed based on gender through the automatic diagnostic system without visiting a specialist physician. For this purpose, the Simple Logistic hybrid system based on the greedy stepwise search algorithm (SLGS) is presented as a novel approach that involves feature analysis to diagnose PD by gender. Six feature groups were extracted from data of 252 subjects and were used to perform this analysis. When the SLGS hybrid system analysis begins, the effective features are first determined by the greedy stepwise search algorithm and diagnosis of PD was made according to the features that were determined using the Simple Logistic classifier. The performance results that were obtained from this system by gender were evaluated using many statistical metrics. The accuracy ratios were 88.71% and 87.15%, with the minimum number of features for males and females, respectively. Additionally, ROC and PRC area values for both genders approached the 0.9 band, showing that the class separation of patients and healthy individuals was nearly perfect. The performance results and perspective of this study were compared with the single published study that used the same data, and the SLGS hybrid system showed higher performance rates than the other study. In addition, the number of the subjects was higher in this study than in other studies and the SLGS system has not been used before in the literature in this field.