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Depression-Level Prediction During COVID-19 Pandemic Among the People of Bangladesh Using Ensemble Technique: MIRF Stacking and MIRF Voting
Anik Pramanik, Md. Hasan Imam Bijoy, Md. Sadekur Rahman ,

Machine learning is currently one of the most prominent approaches for the prediction of different diseases, conditions, and disorders in our human life. In Bangladesh, most people are unaware of their mental health. Only when they suffer from serious mental problems and trauma then they start to take treatment. But if they know about the features which are important to understand, realize, and have to figure out, then the total number of affected people will decrease. The main goal of this research is to predict the depression level of a person using machine learning and data analysis approaches by only filling up 30 basic questionnaires, which are related to depression collected through a public survey. We were able to collect responses of 1088 people from the participants for those 30 instances from all over Bangladesh. To achieve our aim of predicting depression levels, we used a total of ten classifiers, eight of which were based classifiers, which we combined with the best three top-scoring classifiers to build a novel ensemble approach called MIRF Stacking and MIRF Voting ensemble classifier. With 96.78% accuracy, the Random Forest (RF) classifier is the most accurate of the eight base classifiers. Then, our proposed ensemble MIRF Stacking and MIRF Voting classifiers achieve the supremacy performance of 96.81% and 97.18% accuracy, respectively. The proposed method would be used in a framework, where mental health counselors find the root causes and minor explanations for depression in people so that they can better understand all aspects of local psychology and provide them with the best advice and solutions to their problems.

Predict depression level Depression analysis COVID-19 impact MIRF stacking MIRF voting Bangladesh
Journal or Conference Name
Lecture Notes in Networks and Systems
Publication Year