University of Oulu

Riihimaa, P. (2020). Impact of machine learning and feature selection on type 2 diabetes risk prediction. Journal of Medical Artificial Intelligence 3, 10,

Impact of machine learning and feature selection on type 2 diabetes risk prediction

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Author: Riihimaa, Päivi1
Organizations: 1Faculty of Medicine, Center for Health and Technology, Digital Health Hub, University of Oulu, Oulu, PL, Finland
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 0.2 MB)
Persistent link:
Language: English
Published: AME Publishing Company, 2020
Publish Date: 2021-12-08


This survey summarizes the state of the art for type 2 diabetes mellitus (T2DM) prediction and compares the prediction accuracies obtained by conventional statistical regression and machine learning methods, including deep learning. The impact of feature selection and inclusion of clinical and genomic data on T2DM risk prediction accuracy is also reviewed. The results show that there is a tendency that machine learning algorithms outperform logistic regression in the accuracy of T2DM prediction. Inclusion of clinical data and biomarkers to the core feature set improves accuracy, while incorporating genetic markers in the prediction model is still challenging, due to dimensionality problem and the genetic heterogeneity of T2DM.

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Series: Journal of medical artificial intelligence
ISSN: 2617-2496
ISSN-E: 2617-2496
ISSN-L: 2617-2496
Volume: 3
Issue: June
Article number: 10
Type of Publication: A1 Journal article – refereed
Field of Science: 217 Medical engineering
113 Computer and information sciences
3121 General medicine, internal medicine and other clinical medicine
Copyright information: © Journal of Medical Artificial Intelligence. All rights reserved. This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: