Abstract:
This research addresses the early detection of insomnia in Bangladesh, focusing on young
adults affected by extensive phone and social media use. Using machine learning
techniques like Logistic Regression, SVM, Decision Tree, Random Forest, XGBoost,
CatBoost, Naive Bayes, and Light GBM, the study analyzed survey data from university
students. The data processing was conducted using Python and Pandas, with null values
handled carefully. Psychiatric validation was included. The models, especially Logistic
Regression and CatBoost, achieved high accuracy (1.0 in Accuracy, ROC, AUC),
suggesting a strong link between survey symptoms and insomnia. This approach, novel in
Bangladesh, demonstrates machine learning's potential in mental health diagnosis, offering
a cost-effective alternative to traditional methods. The study suggests further research to
expand datasets and tailor models for diverse demographics, integrating these findings into
public health policies.
