Please use this identifier to cite or link to this item:
https://hdl.handle.net/10356/164147
Title: | Missing value imputation for diabetes prediction | Authors: | Luo, Fei Qian, Hangwei Wang, Di Guo, Xu Sun, Yan Lee, Eng Sing Teong, Hui Hwang Lai, Ray Tian Rui Miao, Chunyan |
Keywords: | Engineering::Computer science and engineering | Issue Date: | 2022 | Source: | Luo, F., Qian, H., Wang, D., Guo, X., Sun, Y., Lee, E. S., Teong, H. H., Lai, R. T. R. & Miao, C. (2022). Missing value imputation for diabetes prediction. 2022 International Joint Conference On Neural Networks (IJCNN). https://dx.doi.org/10.1109/IJCNN55064.2022.9892398 | Project: | AISG-GC-2019-003 | Conference: | 2022 International Joint Conference on Neural Networks (IJCNN) | 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. | URI: | https://hdl.handle.net/10356/164147 | ISBN: | 9781728186719 | ISSN: | 2161-4407 | DOI: | 10.1109/IJCNN55064.2022.9892398 | Schools: | School of Computer Science and Engineering | Research Centres: | Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly (LILY) | Rights: | © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/IJCNN55064.2022.9892398. | Fulltext Permission: | open | Fulltext Availability: | With Fulltext |
Appears in Collections: | SCSE Conference Papers |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Missing Value Imputation for Diabetes Prediction.pdf | 289.43 kB | Adobe PDF | View/Open |
SCOPUSTM
Citations
50
3
Updated on Mar 26, 2024
Page view(s)
152
Updated on Mar 28, 2024
Download(s) 50
89
Updated on Mar 28, 2024
Google ScholarTM
Check
Altmetric
Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.