Predicting Health Care Costs Using Evidence Regression

Abstract

People’s health care cost prediction is nowadays a valuable tool to improve accountability in health care. In this work, we study if an interpretable method can reach the performance of black-box methods for the problem of predicting health care costs. We present an interpretable regression method based on the Dempster-Shafer theory, using the Evidence Regression model and a discount function based on the contribution of each dimension. Optimal parameters are learned using gradient descent. The k-nearest neighbors’ algorithm was also used to speed up computations. With the transparency of the evidence regression model, it is possible to create a set of rules based on a patient’s vicinity. When making a prediction, the model gives a set of rules for such a result. We used Japanese health records from Tsuyama Chuo Hospital to test our method, which includes medical checkups, exam results, and billing information from 2016 to 2017. We compared our model to an Artificial Neural Network and Gradient Boosting method. Our results showed that our transparent model outperforms the Artificial Neural Network and Gradient Boosting with an R 2 of 0 . 44 .

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