Decoding Minds: Estimation of Stress Level in Students using Machine Learning

Abstract

Objectives: Develop a predictive model to categorize student’s stress levels and support early interventions based on self-reported data, academic performance, and study load. This will help to receive early diagnosis and treatment. Methods: In this work the data set used was downloaded from a website called KAGGLE. The dataset has more than 6000 samples, the parameters considered in this dataset are Anxiety level, self-esteem, mental_health_history, depression, headache, blood pressure, sleep_quality, breathing_problem, noise_level, living conditions, Safety, basic needs, academic performance, study_load, teacher_student_relationship, future_career_concerns, social support, peer_pressure, extracurricular_activities and bullying which directly or indirectly has an effect on the mental health of the students, so basically here 20 different types of factors are taken into consideration. This specific Research Work employs Machine Learning (ML) approaches to analyze stress levels in students from stress-level text data. Logistic Regression (LR) with 89.46%, KNeighbors with 92.8%, Decision Tree with 94.5%, Random Forest with 95%, and Gradient Boosting with 90.15%, algorithms are used to determine stress levels. Findings: Several significant findings have emerged in this research on predicting mental stress levels in students using machine learning. Studies on feature importance emphasize the importance of sleep quality, depression, mental_health_history, academic performance, and participation in extracurricular activities and several other parameters as critical criteria for accurate prediction. Multimodal techniques that integrate data from mental health history, family history, and academic records provide a more complete picture of a student’s life. Temporal dynamics are important, as stress levels fluctuate throughout time as a result of academic and personal events. Some research goes beyond prediction, investigating intervention options based on tailored stress management suggestions. Novelty: In order to anticipate student’s mental stress, this study presents a novel machine-learning architecture. This methodology attempts to give early identification of students’ mental health at risk by leveraging diverse data sources and using different machine learning algorithms with a very high accuracy level. Keywords: Stress Level, Students, Machine Learning, Decision Tree, Physio Bank