Insomnia Disease Detection Using Machine Learning

Abstract

Insomnia happens to be one common sleep disorder that cuts across many people worldwide. It has left untold suffering in his domains that stretch from physical to mental to emotional health problems. Polysomnography and clinical assessment methods are the commonly known traditional ways of diagnosis, but they happen to be time consuming, resource intensive, and often difficult for many to access. This study introduces the application of machine learning (ML) for an automated, efficient, and scalable approach towards insomnia detection. The physiological and behavioral attributes from students of Daffodil International University, which are collected into a dataset through pre- processing and analysis, are used to train various ML models including Gradient Boosting, Random Forest, and Support Vector Machines. The models were tested against evaluation metrics like accuracy, precision, recall, and F1- score. The best model is Gradient Boosting, which achieved testing accuracy of 99.01%, precision of 99%, and F1-score of 99%. Some of the major challenges addressed in this study are imbalanced datasets, complex model interpretability, and ethical considerations such as data privacy. By these results, machine learning turned out to be a feasible option for early detection and accurate latticing of insomnia. Such a system has the potential to revolutionize healthcare by providing accessible, noninvasive, and cost-effective diagnostic tools, thereby improving patient outcomes and advancing the role of ML in sleep medicine.