Depression Prediction Using Machine Learning Techniques

Depression, a prevalent mental health disorder, presents significant challenges in timely diagnosis and intervention, emphasizing the importance of early detection for effective treatment and management. This chapter aims to introduce the significance of early examination mitigating the adverse effects of depression. A thorough literature review is conducted to synthesize existing research on depression prediction, focusing on the evolution of ML techniques and their applications in mental health diagnostics. Key concepts elucidate the theoretical foundations of algorithms such as k-NN, logistic regression, random forest, DT, and support vector machines in the context of depression prediction. This study presents a comparative study of various ML algorithms for predicting depression. The performance of k-NN, logistic regression, DT, random forest, support vector machine, and other relevant algorithms in accurately diagnosing depression based on patient data is evaluated.