The Role of Machine Learning in Reducing Healthcare Costs: The Impact of Medication Adherence and Preventive Care on Hospitalization Expenses
This work addresses healthcare cost reduction for patients and providers, but it is incremental as it applies standard machine learning models to a specific dataset without methodological innovation.
This study tackled the problem of predicting hospitalization risk to reduce healthcare costs by analyzing medication adherence and preventive care, finding that high adherence and preventive care can reduce hospitalization risk by 38.3% and 37.7% respectively, with a Gradient Boosting model achieving 81.2% accuracy.
This study reveals the important role of prevention care and medication adherence in reducing hospitalizations. By using a structured dataset of 1,171 patients, four machine learning models Logistic Regression, Gradient Boosting, Random Forest, and Artificial Neural Networks are applied to predict five-year hospitalization risk, with the Gradient Boosting model achieving the highest accuracy of 81.2%. The result demonstrated that patients with high medication adherence and consistent preventive care can reduce 38.3% and 37.7% in hospitalization risk. The finding also suggests that targeted preventive care can have positive Return on Investment (ROI), and therefore ML models can effectively direct personalized interventions and contribute to long-term medical savings.