Fusion of Heterogeneous Features for Major Depression Disorder Classification based on QDM-Ranked Genetic Algorithm

Tsu-Wang Shen, Fang-Chih Liu, and Shao-Tsu W. Chen

Keywords

Quartile Discriminant Measurement QDM, Major Depression Disorder (MDD), Heterogeneous Features, Genetic Algorithm (GA)

Abstract

Major depression disorder is a mental disorder which impacts various aspects of society. Fusion of heterogeneous features from different signal sources is a challenge task, especially with some flicking features different from persons or physiological conditions. The aim of this research is to develop a fusion model based on correlation, quartile discriminant measurement, and genetic algorithm. The result indicates heterogeneous features successfully fused for MDD classification.Finally, it provides 100% and 70% accuracy for training and testing data sets, respectively.

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