Abstract—Structural MRI offers anatomical details and high sensitivity to pathological changes. It can demonstrate certain patterns of brain changes present at a structural level. Research to date has shown that volumetric analysis of brain regions has importance in depression detection. However, such analysis has had very minimal use in depression detection studies at individual level. Optimally combining various brain volumetric features/attributes, and summarizing the data into a distinctive set of variables remain difficult. This study investigates machine learning algorithms that automatically identify relevant data attributes for depression detection. Different machine learning techniques are studied for depression classification based on attributes extracted from structural MRI (sMRI) data. The attributes include volume calculated from whole brain, white matter, grey matter and hippocampus. Attributes subset selection is performed aiming to remove redundant attributes using three filtering methods and one hybrid method, in combination with ranker search algorithms. The highest average classification accuracy, obtained by using a combination of both SVM-EM and IG-Random Tree algorithms, is 85.23%. The classification approach implemented in this study can achieve higher accuracy than most reported studies using sMRI data, specifically for detection of depression.

Index Terms—MRI, brain image analysis, image feature selection, machine learning, depression detection.

K. Kipli and A. Z. Kouzani are with the School of Engineering, Deakin University, Waurn Ponds, Victoria 3216, Australia (e-mail: kkipli@deakin.edu.au).
I. R. A. Hamid is with the School of Information Technology, Deakin University, Waurn Ponds, Victoria 3216, Australia (email: iraha@deakin.edu.au).

Cite:Kuryati Kipli, Abbas Z. Kouzani, and Isredza Rahmi A. Hamid, "Investigating Machine Learning Techniques for Detection of Depression Using Structural MRI Volumetric Features," International Journal of Bioscience, Biochemistry and Bioinformatics vol. 3, no. 5, pp. 444-448, 2013.