Missing Value Imputation for Diabetes Prediction

Abstract

Machine learning (ML) models have been widely used to improve the accuracy and efficiency of various types of disease diagnostic tasks. However, it is still challenging to apply ML models to perform diabetes-related prediction tasks mainly because patients' health records are sparse and have a vast amount of missing values. Missing values often break the diabetes prediction pipelines, posing challenges to existing approaches. Such problem deteriorates significantly when critical attribute values (e.g., blood test results on HbAlc, FPG and OGTT2hr) are missing. In this paper, we introduce a large-scale diabetes-related dataset named Chronic Disease Management System (CDMS) dataset, which collects the clinical records of more than 700,000 visits of over 65,000 patients across eight years. CDMS is anonymously collected and has a high percentage of missing values on several critical attributes for diabetes prediction. If not being dealt with carefully, the missing values will cause significant performance degradation of the applied ML models. In this paper, we also investigate the effectiveness of multiple data imputation methods through conducting extensive experiments using CDMS. Experimental results show that k-Nearest Neighbor Imputation (KNNI) performs better than other methods in this diabetes prediction task. Specifically, with KNNI applied, the diabetes prediction accuracy and precision are both over 0.8 using various ML predictive models.