Predicting Medical Costs Using Machine Learning: A Data-Driven Approach

The UK is currently seeing rapid growth in private healthcare services. This growth is complemented by increased demand for private healthcare as well, as per The Guardian, consumer expenditure on private healthcare ranges from 3,200 pounds to 15, 075 pounds [1]. Granted that this growth in private healthcare can benefit the increasing demand and supply, these private medical practitioners must maintain financial transparency between their patients. Private medical services are deemed to be significantly costlier than public healthcare, which is why to assure the patient that private institutes are not exploiting consumer expenditure on healthcare, they must provide the patient with an estimated total cost of the medical service beforehand. Subsequently, patients can decide based on the urgency of their medical situation when and where to get treated. The prediction of the total cost of a medical service can be done by analyzing the data of a patient's medical history. Given that the prediction of the total cost requires the analysis and processing of data, it can be argued that the most efficient method of prediction would be via a machine learning model. Hence, this paper will compare the performance of a ridge regression machine learning model, the polynomial regression model, and a random forest regressor. The dataset used for this comparison is constant for both models and was taken from Kaggle. To measure the accuracy of the two models for their comparison, they will be tested based on their accuracy, mean absolute error, and root mean squared error to compare the results of the two models. After analyzing the data collected, this paper concluded that the best performing model was the random forest regressor with the highest accuracy of 87.5%. These reports suggest that machine learning models could be the solution to financial transparency in private medical institutes, and the utilization of a random forest regressor for this task seems to be the best fit.