Improving mental health and suggesting coping techniques to IT Professionals by utilizing Random Forest based algorithm

Improving mental health and suggesting coping techniques to IT Professionals by utilizing Random Forest based algorithm

Chitralekha Dwivedi
Asst. Prof., Department of Computer Engineering, Dr. D.Y. Patil Institute of Technology, Pune chitralekha.dwivedi@dypvp.edu.in

Nishant Surve, Rohit Mehta, Omkar Nawale, Rushikesh Shinde
Final Year Student, Department of Computer Engineering, Dr. D.Y. Patil Institute of Technology, Pune
nishantsurve254@gmail.com

Abstract

Mental health challenges like chronic stress are increasingly affecting professionals in the tech industry, lowering both productivity and life quality. This study presents an AI-driven approach for real-time stress prediction and tailored interventions. Using supervised learning models, including Random Forest, SVM, and Neural Networks; the system categorizes stress levels by analysing behavioural and contextual inputs collected from self-assessments. Based on the predicted stress level (low, moderate, or high), the system suggests personalized remedies like mindfulness sessions, scheduled pauses or focus-enhancing tips. A dashboard tracks stress trends to help users recognize and manage their patterns. The model demonstrates high accuracy and is scalable for real-world application, prioritizing user privacy and adaptability in both personal and professional settings.

Index Terms—Stress prediction, machine learning, personalized recommendations, mental health, automation, stress analytics.