Evaluating Machine Learning Models for Healthcare Services Efficiency
DOI:
https://doi.org/10.14571/brajets.v16.n4.1280-1289Resumo
The article is dedicated to the problem of assessing the quality of healthcare using machine learning algorithms. It is proposed to evaluate categorical term “healthcare quality” as discrete variable on the basis of data on treatment result, provided by one of the major hospitals in Russia. Preliminary analysis of both dependent and explanatory variables is provided. The statistics of results of treatment depending on the age and gender of patients are analyzed. The necessity of reducing the initial dataset is justified. The ways of correction the imbalance in dataset is proposed, such as SMOTE and Tomek Links On the basis of corrected data ordered logit and Random Forest models were designed. The comparative analysis of various models is presented. The drawbacks of each model are explained. The ordered logit model on balanced data allowed to determine factors, that have greatest positive impact on the treatment result as well as Random Forest model made the predictions as accurate as it is possible. On the basis of results obtained, the recommendations to improve the healthcare efficiency in the framework of particular hospital are made.Referências
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