Previously submitted to: JMIR Formative Research (no longer under consideration since Jul 24, 2025)
Date Submitted: Jan 3, 2025
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
From Data to Decisions: Predicting Patient Health Outcomes and Risk Using Machine Learning
ABSTRACT
Predicting health outcomes and classifying patient risks are critical for improving healthcare decision-making. Using a dataset of 30 clinical and lifestyle variables, this study employs a hybrid approach integrating statistical and machine learning models to analyze patient health metrics. Linear regression with interaction terms achieved an adjusted R2 of 0.84 for predicting net health score change (Y1), and deviations from normality prompted exploration of machine learning models, with Random Forest emerging as the most robust alternative. For risk classification (Y2), logistic regression outperformed decision trees, achieving 94.25% accuracy and an AUC of 0.9847, identifying heart rate and blood pressure changes as key predictors. This study demonstrates the power of tailored feature selection and predictive modeling in deriving actionable insights for personalized healthcare.
Citation
Request queued. Please wait while the file is being generated. It may take some time.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.