Exploratory Data Analysis and Machine Learning Approaches for Early Detection of Student Depression

Muhammad Fikry (Universitas Malikussaleh)
Bustami Bustami (Universitas Malikussaleh)
Ella Suzanna (Universitas Malikussaleh)

Proceeding of The International Conference on Electrical Engineering and Informatics
Vol. 1 No. 2 (2024): July : Proceeding of the International Conference on Electrical Engineering and

This study conducts an exploratory data analysis combined with machine learning techniques to identify early signs of student depression. We investigated various factors affecting mental health among students, including sleep duration, dietary patterns, history of suicidal thoughts, family history of mental illness, and their relationships with depression across age groups and academic pressure. The study also examined the influence of gender on academic stress levels. Three machine learning models such as Random Forest, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN) were utilized to predict depression. The performance of these models was evaluated, achieving accuracy rates of 84.97% for Random Forest, 84.85% for SVM, and 81.16% for KNN. The findings highlight the effectiveness of these models in predicting student depression and underscore the importance of targeted mental health interventions based on key factors influencing mental health among students.