Predictive ability of current machine learning algorithms for type 2 diabetes mellitus: A meta‐analysis Article Swipe
Satoru Kodama
,
Kazuya Fujihara
,
Chika Horikawa
,
Masaru Kitazawa
,
Midori Iwanaga
,
Kiminori Kato
,
Kenichi Watanabe
,
Yoshimi Nakagawa
,
Takashi Matsuzaka
,
Hitoshi Shimano
,
Hirohito Sone
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1111/jdi.13736
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1111/jdi.13736
Aims/Introduction Recently, an increasing number of cohort studies have suggested using machine learning (ML) to predict type 2 diabetes mellitus. However, its predictive ability remains inconclusive. This meta‐analysis evaluated the current ability of ML algorithms for predicting incident type 2 diabetes mellitus. Materials and Methods We systematically searched longitudinal studies published from 1 January 1950 to 17 May 2020 using MEDLINE and EMBASE. Included studies had to compare ML’s classification with the actual incidence of type 2 diabetes mellitus, and present data on the number of true positives, false positives, true negatives and false negatives. The dataset for these four values was pooled with a hierarchical summary receiver operating characteristic and a bivariate random effects model. Results There were 12 eligible studies. The pooled sensitivity, specificity, positive likelihood ratio and negative likelihood ratio were 0.81 (95% confidence interval [CI] 0.67–0.90), 0.82 [95% CI 0.74–0.88], 4.55 [95% CI 3.07–6.75] and 0.23 [95% CI 0.13–0.42], respectively. The area under the summarized receiver operating characteristic curve was 0.88 (95% CI 0.85–0.91). Conclusions Current ML algorithms have sufficient ability to help clinicians determine whether individuals will develop type 2 diabetes mellitus in the future. However, persons should be cautious before changing their attitude toward future diabetes risk after learning the result of the diabetes prediction test using ML algorithms.
Related Topics
Concepts
Medicine
False positive paradox
Algorithm
Receiver operating characteristic
Confidence interval
Diabetes mellitus
Meta-analysis
Type 2 Diabetes Mellitus
Type 2 diabetes
Machine learning
Incidence (geometry)
MEDLINE
Internal medicine
Artificial intelligence
Mathematics
Endocrinology
Computer science
Geometry
Political science
Law
Metadata
- Type
- review
- Language
- en
- Landing Page
- https://doi.org/10.1111/jdi.13736
- OA Status
- gold
- Cited By
- 36
- References
- 42
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4200282090
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W4200282090Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1111/jdi.13736Digital Object Identifier
- Title
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Predictive ability of current machine learning algorithms for type 2 diabetes mellitus: A meta‐analysisWork title
- Type
-
reviewOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-12-23Full publication date if available
- Authors
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Satoru Kodama, Kazuya Fujihara, Chika Horikawa, Masaru Kitazawa, Midori Iwanaga, Kiminori Kato, Kenichi Watanabe, Yoshimi Nakagawa, Takashi Matsuzaka, Hitoshi Shimano, Hirohito SoneList of authors in order
- Landing page
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https://doi.org/10.1111/jdi.13736Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://doi.org/10.1111/jdi.13736Direct OA link when available
- Concepts
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Medicine, False positive paradox, Algorithm, Receiver operating characteristic, Confidence interval, Diabetes mellitus, Meta-analysis, Type 2 Diabetes Mellitus, Type 2 diabetes, Machine learning, Incidence (geometry), MEDLINE, Internal medicine, Artificial intelligence, Mathematics, Endocrinology, Computer science, Geometry, Political science, LawTop concepts (fields/topics) attached by OpenAlex
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36Total citation count in OpenAlex
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2025: 11, 2024: 8, 2023: 10, 2022: 6, 2021: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.diabetes | 19, 41, 78, 186, 202, 210 |
| abstract_inverted_index.eligible | 121 |
| abstract_inverted_index.incident | 38 |
| abstract_inverted_index.interval | 138 |
| abstract_inverted_index.learning | 13, 205 |
| abstract_inverted_index.mellitus | 187 |
| abstract_inverted_index.negative | 131 |
| abstract_inverted_index.positive | 127 |
| abstract_inverted_index.receiver | 108, 160 |
| abstract_inverted_index.searched | 48 |
| abstract_inverted_index.studies. | 122 |
| abstract_inverted_index.Materials | 43 |
| abstract_inverted_index.Recently, | 2 |
| abstract_inverted_index.bivariate | 113 |
| abstract_inverted_index.determine | 179 |
| abstract_inverted_index.evaluated | 29 |
| abstract_inverted_index.incidence | 74 |
| abstract_inverted_index.mellitus, | 79 |
| abstract_inverted_index.mellitus. | 20, 42 |
| abstract_inverted_index.negatives | 92 |
| abstract_inverted_index.operating | 109, 161 |
| abstract_inverted_index.published | 51 |
| abstract_inverted_index.suggested | 10 |
| abstract_inverted_index.algorithms | 35, 172 |
| abstract_inverted_index.clinicians | 178 |
| abstract_inverted_index.confidence | 137 |
| abstract_inverted_index.increasing | 4 |
| abstract_inverted_index.likelihood | 128, 132 |
| abstract_inverted_index.negatives. | 95 |
| abstract_inverted_index.positives, | 88, 90 |
| abstract_inverted_index.predicting | 37 |
| abstract_inverted_index.prediction | 211 |
| abstract_inverted_index.predictive | 23 |
| abstract_inverted_index.sufficient | 174 |
| abstract_inverted_index.summarized | 159 |
| abstract_inverted_index.Conclusions | 169 |
| abstract_inverted_index.algorithms. | 215 |
| abstract_inverted_index.individuals | 181 |
| abstract_inverted_index.3.07–6.75] | 148 |
| abstract_inverted_index.hierarchical | 106 |
| abstract_inverted_index.longitudinal | 49 |
| abstract_inverted_index.sensitivity, | 125 |
| abstract_inverted_index.specificity, | 126 |
| abstract_inverted_index.0.13–0.42], | 153 |
| abstract_inverted_index.0.67–0.90), | 140 |
| abstract_inverted_index.0.74–0.88], | 144 |
| abstract_inverted_index.0.85–0.91). | 168 |
| abstract_inverted_index.inconclusive. | 26 |
| abstract_inverted_index.respectively. | 154 |
| abstract_inverted_index.characteristic | 110, 162 |
| abstract_inverted_index.classification | 70 |
| abstract_inverted_index.systematically | 47 |
| abstract_inverted_index.meta‐analysis | 28 |
| abstract_inverted_index.Aims/Introduction | 1 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 89 |
| corresponding_author_ids | https://openalex.org/A5042086791 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 11 |
| corresponding_institution_ids | https://openalex.org/I71395657 |
| citation_normalized_percentile.value | 0.96765857 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |