Classification of Renal Function Trends in Time Series Data Using Machine Learning

Abstract

As a preventive measure for chronic kidney disease requiring dialysis, some municipal governments and medical associations make comments to family doctors and examinees based on the results of health checkups. However, the judgment and preparation of comments are mostly done manually by physicians and public health nurses and require much labor. In this study, we examine the trend determination of kidney function level using machine learning as part of the study to realize the automation of this task. We propose a preprocessing method for time-series data dealing with different numbers of checkups and different duration of checkups. As a machine learning model, we propose ensemble learning methods using gradient boosting decision trees. The effectiveness of the proposed methods is demonstrated in evaluation using about 3,000 cases of specific health checkup data.