Predicting Patient Recovery Time Using Clinical and Lifestyle Variables: A Statistical Approach

Abstract

Understanding the factors influencing patient recovery time is essential for improving healthcare outcomes. This study applies a Multiple Linear Regression (MLR) model to predict recovery time using clinical (Age, BMI, Severity Score, Hospital Stay, Underlying Conditions, Medication Type) and lifestyle factors (Smoking, Alcohol Consumption, Physical Activity). A dataset covering a 10-year period (2014–2023) was analyzed. The MLR results reveal that Severity Score (β = 5.0021, p < 0.001) and Hospital Stay (β = 0.8052, p < 0.001) are the strongest predictors of recovery time. Lifestyle choices also significantly impact recovery: smoking (β = 1.5634, p < 0.001) and alcohol consumption (β = 1.4082, p < 0.001) extend recovery time, while higher physical activity (β = -0.9352, p = 0.0012) speeds it up. The model explains 98.5% of the variance (R² = 0.985) and has a low RMSE (1.91 days), indicating high accuracy. The findings highlight the need for personalized treatment plans that consider both medical conditions and lifestyle habits. Healthcare providers can use this model to predict recovery time more effectively and design interventions that encourage healthy lifestyle changes. Future research should explore non-linear effects and integrate additional biological markers for enhanced predictive accuracy.

Keyword: Clinical Variables, Healthcare, Lifestyle Factors, Multiple Linear Regression,

Predictive Modeling, Recovery Time