A clinical support system for classification and prediction of depression using machine learning methods

The health sector collects a very large amount of data, hence the diagnostic process processes a very large and varied amount of data type which makes the process of analyzing these data very complicated, specifically the healthcare sector, mental health is very composed and varied by various data criteria. However, the forecast of health in modern life becomes very important. To this end, the proposed work aims to analyze patient data based on their represented symptoms, in order to help clinicians and mental health practitioners classify and refine the type of depression disorder "characterized" in patients intelligently, in order to make a relevant decision. In this context, the proposed system called CP-DDC is based on machine learning algorithms supervised more precisely by the random-forest algorithm. The dataset used in the case study contains 150 instances and 11 attributes, which define the different patient criteria, obtained from the Mohammed VI University Hospital Center of Marrakech "CHU." The results of the experiment show that the proposed system offers the highest performance.