Nayyem, Mohammad NavidRaju, Md Azad HossainAl Rakin, AbdullahSharif, Kazi ShaharairArafin, RudmilaSultana, Sharmin2026-07-092026-07-091/1/2024M. N. Nayyem, M. A. H. Raju, A. Al Rakin, K. S. Sharif, R. Arafin and S. Sultana, "Augmenting Sleep Quality Prognostics through Internet of Things and Machine Learning: A Rigorous Comparative Analysis for Advanced Personalized Health Metrics," 2024 7th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI), Yogyakarta, Indonesia, 2024, pp. 310-315, doi: 10.1109/ISRITI64779.2024.10963636.9.79833E+122-s2.0-105004415417https://hdl.handle.net/10361/28498This study investigates the potential of integrating Internet of Things (IoT) devices with advanced machine learning models to enhance sleep quality assessment. Leveraging a dataset of 826 individuals collected through wearable IoT devices, we perform a comparative analysis across four machine learning algorithms: Logistic Regression, Support Vector Machine (SVM), Artificial Neural Network (ANN), and Random Forest. Our methodology includes comprehensive data preprocessing, feature engineering with a novel Sleep Quality Score (SQS) formulation, and extensive model evaluation through cross-validation. Experimental results reveal that the Random Forest model achieves the highest accuracy at 99%, followed closely by SVM at 98% and ANN at 96%. Key factors impacting sleep quality, such as sleep duration, efficiency, and various sleep stage ratios, are identified and analyzed. Our findings contribute significantly to the field of IoT-based health monitoring, offering insights for developing tailored interventions and improving sleep management. This research lays the groundwork for future advancements in smart health applications and personalized healthcare solutions.310-315en-USFALSEArtificial neural networkInternet of thingsLogistic regressionMachine learningRandom forestSleep qualitySupport vector machineComputational intelligence.Artificial intelligence.Internet of things.Logistic regression analysis.Support vector machines.Conference Proceedings10.1109/ISRITI64779.2024.10963636