Comparing Logistic Regression and Artificial Neural Network Models for Analyzing Medicare Utilization and Costs Among Older Adults.

Abstract

Background: Artificial neural networks (ANNs) are increasingly applied in health care outcome prediction, yet their relative benefits compared with traditional methods in health services research remain unclear.

Objective: To examine health care utilization and costs among community-dwelling older adults using the Andersen Behavioral Model, and to compare the performance of logistic regression and ANN models.

Research design: Cross-sectional study utilizing linked data from CMS Medicare fee-for-service (FFS) claims and Consumer Assessment of Healthcare Providers and Systems (CAHPS) surveys (2018-2022). The sample included 254,748 Medicare beneficiaries aged 65 and older. Outcomes were high Medicare costs (top 25%), 30-day readmissions, and preventable hospitalizations (PQIs). Predictors included socioeconomic factors, chronic conditions, and patient-reported measures. Model performance was assessed using the area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and Brier scores.

Results: Chronic conditions, including heart disease and depression, significantly predicted higher Medicare costs. Poor self-rated health, functional limitations, dual eligibility, and lower educational attainment correlated strongly with readmissions and preventable hospitalizations. ANN and logistic regression models demonstrated comparable performance across outcomes, with similar AUC, sensitivity, specificity, PPV, NPV, and Brier scores.

Conclusions: Both logistic regression and ANN models effectively predict health care utilization and high-risk outcomes among older adults using structured Medicare data. Logistic regression offers interpretability and robust predictive power, whereas ANN models may provide additional value as healthcare datasets grow increasingly complex and comprehensive.