Classification of Kidney Function Level from Time Series Data Using Machine Learning

Abstract

In this study, we aim to construct effective classification models of renal function level using machine learning, which can be used by physicians in the diagnosis of renal function. We propose a data preprocessing method for the constructed classification models to deal with different numbers and different periods of medical checkups. As machine learning models, we use gradient boosting decision tree models and propose several ensemble learning methods. The effectiveness of the proposed methods is demonstrated by simulation results with about 3,000 cases of medical checkup data.