Evaluating Hospital Case Cost Prediction Models Using Azure Machine Learning Studio
This provides a practical tool for healthcare financial management, but it is incremental as it applies existing methods to a new dataset without introducing novel algorithms.
The researchers tackled the problem of predicting hospital case costs by developing an Azure Machine Learning Studio tool to rapidly assess 14 regression models, finding that robust regression, boosted decision tree regression, and decision forest regression performed best based on five performance metrics.
Ability for accurate hospital case cost modelling and prediction is critical for efficient health care financial management and budgetary planning. A variety of regression machine learning algorithms are known to be effective for health care cost predictions. The purpose of this experiment was to build an Azure Machine Learning Studio tool for rapid assessment of multiple types of regression models. The tool offers environment for comparing 14 types of regression models in a unified experiment: linear regression, Bayesian linear regression, decision forest regression, boosted decision tree regression, neural network regression, Poisson regression, Gaussian processes for regression, gradient boosted machine, nonlinear least squares regression, projection pursuit regression, random forest regression, robust regression, robust regression with mm-type estimators, support vector regression. The tool presents assessment results arranged by model accuracy in a single table using five performance metrics. Evaluation of regression machine learning models for performing hospital case cost prediction demonstrated advantage of robust regression model, boosted decision tree regression and decision forest regression. The operational tool has been published to the web and openly available for experiments and extensions.