The health care costs constitute a significant fraction of the U.S. economy. Nearly 20% of the Gross Domestic Product (GDP) is spent on health care. The health spending in the US is the highest among all developed nations in absolute numbers as well as a percentage of the economy. The U.S. government bears a large portion of seniors’ health expenditure through its Medicare program. The growing health related expenses combined with the fact that the baby-boomer generation is retiring, and hence they will be eligible for Medicare, puts a great burden on the U.S. exchequer. Therefore, it is essential to contain health related payments through all means possible. In this work, we will develop a medical price prediction system using machine learning algorithms which will aid in steering patients to cost effective providers and thereby curb health spending. The policymakers can also use the tool to better understand which providers are relatively expensive and take punitive actions if necessary. The prediction of the medical price will be done using implementing Random Forest Regression algorithm in machine learning. Additionally, we plan to include the experiments on the same data with other machine learning models such as Gradient Boosted Trees and Linear Regression and compare results. The findings from these experiments will also be included.
Tike, Anuja, "A MEDICAL PRICE PREDICTION SYSTEM" (2018). Master's Projects. 619.