Multiple Linear Regression Model for Predicting Sleep Quality Based on Lifestyle
DOI:
https://doi.org/10.63643/jodens.v6i1.387Keywords:
Quality of Sleep, Multiple Linear Regression, Machine Learning, Lifestyle, Model Interpretation.Abstract
Sleep disorders significantly impact individuals' quality of life and chronic health. Previous research utilizing the Sleep Health and Lifestyle Dataset predominantly focused on Classification tasks—predicting discrete diagnostic categories such as Insomnia or Sleep Apnea. Although classification accuracy has been high, this method fails to provide a continuous, quantitative assessment of the severity of Quality of Sleep (QOS). This study aims to address this limitation by developing and interpreting a Multiple Linear Regression (MLR) model to predict the numeric Quality of Sleep (QOS) score on a 1-10 scale based on lifestyle, demographic, and biometric factors. The MLR model was applied following pre-processing, which included One-Hot Encoding for categorical variables and the removal of diagnostic variables to prevent data leakage. Evaluation results demonstrate that the model achieved excellent performance, confirmed by a high Coefficient of Determination (R2) of 0.957 and a very low Mean Absolute Error (MAE) of 0.145 units. Quantitative analysis of the regression coefficients identified Sleep Duration as the most dominant positive predictor and Stress Level as the most significant negative predictor of QOS. These findings provide an important contribution in the form of an interpretable mathematical equation, which can be utilized by clinicians to make measurable, evidence-based intervention recommendations, shifting the focus from diagnosis to quantitative management and prevention.
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