Predicting asthma patients' total cost using neural networks and linear regression

Abstract

In this paper, we analyze a population of asthma patients trying to predict the total cost of their treatment based on various demographic, clinical, and pharmacological data. We are comparing a neural network architecture with a simple linear regression using data from a healthcare insurance provider based in Louisiana. Our first focus was to explore the factors associated with the total cost of asthma treatment. Then we identified a sufficient threshold of data for which the neural networks outperform the linear regression models in terms of predictive accuracy. We showed that even with a simple Neural Network architecture, after approximately 6,000 randomly selected data points Neural Networks outperform Linear Regression almost always.