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: | http://urn.fi/urn:nbn:fi-fe2021120859498 |
Language: | English |
Published: |
AME Publishing Company,
2020
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Publish Date: | 2021-12-08 |
Description: |
AbstractThis 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. see all
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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 |
DOI: | https://doi.org/10.21037/jmai-20-4 |
OADOI: | https://oadoi.org/https://doi.org/10.21037/jmai-20-4 |
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 |
Subjects: | |
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: https://creativecommons.org/licenses/by-nc-nd/4.0/. |
https://creativecommons.org/licenses/by-nc-nd/4.0/ |