Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{192732,
        author = {Ganesh Ishwarchandra Ghonsikar and Rucha Ravindra Galgali},
        title = {Sleep Disorder Prediction Using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {9},
        pages = {2031-2035},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=192732},
        abstract = {sleep disorder prediction involves identifying early signs that a person may develop problems with sleep, such as insomnia, sleep apnea, or restless leg syndrome. It relies on monitoring patterns in sleep duration, quality, and consistency. Factors like stress levels, irregular work schedules, lifestyle habits, and medical history can indicate higher risk. Physical symptoms such as excessive daytime sleepiness, difficulty falling asleep, or frequent awakenings are important clues. Regular tracking of these signals can help anticipate disorders before they become severe. Early prediction allows for timely lifestyle adjustments, medical consultations, and preventive measures to maintain healthy sleep. In this study, multiple machine learning algorithms are evaluated, including Random Forest, K-Nearest Neighbours, Support Vector Machine (RBF), Multilayer Perceptron (Neural Network), Decision Tree, XGBoost, Gradient Boosting, and Logistic Regression. The results show that tree-based ensemble methods, particularly Random Forest, Gradient Boosting, and XGBoost, delivered the best performance, achieving an accuracy of 94.7% and a weighted F1-score of 0.95, with balanced precision and recall across all sleep disorder categories. Among these, XGBoost emerged as the preferred model due to its strong generalization and robustness. Early prediction using such models can enable timely lifestyle adjustments, medical consultation, and preventive interventions to maintain healthy sleep.},
        keywords = {sleep disorder prediction, insomnia, sleep apnea, restless leg syndrome, sleep quality, early detection, preventive interventions, machine learning, Random Forest, XGBoost, Gradient Boosting, Decision Tree, K-Nearest Neighbours, Support Vector Machine (RBF), Multilayer Perceptron (Neural Network), Logistic Regression, sleep health.},
        month = {February},
        }