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AMLDD: An Automated Machine Learning Framework for Depression Detection Among Undergraduate Engineering Students in India

Tapan Kumar, Rahul Kumar Jha, Altaf Alam, Prince Kumar, Krish Kumar Bharti, Radheshyam Kumar
Assistant Professor (Tapan Kumar), UG Scholars: Rahul Kumar Jha, Altaf Alam, Prince Kumar, Krish Kumar Bharti, Radheshyam Kumar
Department of CSE, Purnea College of Engineering, Purnea, Bihar, Bihar Engineering University, Patna, DSTTE, Patna, Bihar- 800015, India
tapan.dstte@bihar.gov.in, rahuljha0119@gmail.com, altafalam732300@gmail.com, chapraprince123@gmail.com, krrishkr21bh@gmail.com, kumarradhebhai407@gmail.com
How to cite: Tapan Kumar, Rahul Kumar Jha, Altaf Alam, Prince Kumar, Krish Kumar Bharti, Radheshyam Kumar, AMLDD: An Automated Machine Learning Framework for Depression Detection Among Undergraduate Engineering Students in India, Sciences Methods and Technologies International Journal (SciMeTech), Vol 2, Issue 2, p 18-24
Abstract
Stress is a natural sentiment that impacts humans physically and mentally. It is above the physical pain that humans can bear. Stress is the main cause of depression. The research work presents an overview of stress management using Artificial Intelligence. It focuses on an experiment conducted on the IBM Cloud Platform using automated machine learning tools to detect early signs of sadness among the undergraduate students. The experimental results show that logistic regression achieved the highest accuracy of 0.861 with feature engineering and hyperparameter optimization. The presented work can be generalized to other categories to help society, especially college students aged 18 and above, and could save their lives.
Keywords: Depression, Students, Stress management, Automated Machine Learning, Artificial Intelligence

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SciMeTech · Vol 2, Issue 2 · p 18-24