Multi-Classification of Depression Levels Based on Blood Biomarkers

Depression is a psychiatric condition characterized by a persistent feeling of sadness and diminished interest in significant activities. Diagnosing this health problem is challenging because it relies on several social and physiological factors. The timely identification of depression aids in the prevention of severe outcomes, such as suicide. Early detection of depression levels is crucial to prevent adverse effects and enhance the quality of everyday life. The purpose of this work is to use data preprocessing techniques to create a clean dataset from noisy and incomplete medical data that includes blood biomarker values of patients and then apply data mining techniques to this dataset to predict the degree of depression. Adana Dr. Ekrem Tok Mental Health Hospital supplied the raw data for the study, with consent from the ethics committee. Missing data are completed by filling with the constant value and the most frequent value methods (i.e., 0 and mode values) in the relevant column. The classification of the dataset is performed using AdaBoost, Decision Tree (DT), and Logistic Regression (LR) classifiers, which have been previously used in medical datasets and demonstrated to be effective. The Logistic Regression classifier achieved the highest success rate (Accuracy: 0.541 and weighted F-score: 0.481).