A Machine Learning Framework for Predicting Functional Status and Estimating Health Care Costs in Pulmonary Arterial Hypertension: Retrospective Cohort Study.

BACKGROUND: Pulmonary arterial hypertension (PAH) is a rare and progressive disease associated with substantial clinical heterogeneity and high health care costs. While machine learning (ML) has been increasingly applied to predict clinical outcomes, approaches that integrate clinical prediction with downstream economic modeling remain limited. OBJECTIVE: This study proposes and evaluates an ML-based framework that combines prediction of future functional status with estimation of associated health care costs in patients with PAH. METHODS: In this retrospective cohort study, data from 181 patients with PAH were analyzed. A total of 6 supervised ML algorithms (Least Absolute Shrinkage and Selection Operator [LASSO] regression, ridge regression, k-nearest neighbors, decision trees, random forest, and gradient boosting) were trained to predict the relative change in World Health Organization functional class (WHO-FC) from baseline to 12-month follow-up using baseline clinical variables. Predicted change rates were subsequently converted to predicted WHO-FC values at the 12-month follow-up for downstream health care cost estimation. Missing values in predictor variables were imputed using median substitution. Median imputation was performed before the dataset was split into training and test sets. Model performance was evaluated using Pearson correlation coefficient (r), and error metrics (RMSE, MAE, and MedAE), based on cross-validation and independent test set validation. Predicted WHO-FC values were subsequently linked to externally validated, stratified real-world health care cost estimates to approximate future economic burden. RESULTS: Among the evaluated models, k-nearest neighbors showed the highest predictive performance (r=0.40; R2=0.16), followed by LASSO regression (r=0.30; R2=0.09). Prediction errors were comparable across models. Estimated mean health care costs ranged from US $12,481 to $12,614 per patient per month. Despite a significant improvement in WHO-FC over 12 months, estimated health care costs remained largely unchanged, reflecting persistent economic burden across functional class strata. CONCLUSIONS: This study demonstrates the feasibility of an ML-based framework that integrates prediction of functional status with health care cost estimation in PAH. While predictive performance was moderate, the approach illustrates how ML models can be extended beyond clinical end points toward system-level applications such as cost forecasting and resource planning. Further validation in larger, multicenter cohorts is required before clinical or health system implementation.