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Recently, one of these studies found five diabetes subgroups with varying risks of complications and treatment responses. Here, we tackle the development and assessment of different models for classifying Type 2 Diabetes (T2DM) subtypes through machine learning approaches, with the aim of providing a performance comparison and new insights on the matter.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>We developed a three-stage methodology starting with the preprocessing of public databases NHANES (USA) and ENSANUT (Mexico) to construct a dataset with <jats:italic>N<\/jats:italic> = 10,077 adult diabetes patient records. We used <jats:italic>N<\/jats:italic> = 2,768 records for training\/validation of models and left the remaining (<jats:italic>N<\/jats:italic> = 7,309) for testing. In the second stage, groups of observations \u2013each one representing a T2DM subtype\u2013 were identified. We tested different clustering techniques and strategies and validated them by using internal and external clustering indices; obtaining two annotated datasets <jats:italic>Dset A<\/jats:italic> and <jats:italic>Dset B<\/jats:italic>. In the third stage, we developed different classification models assaying four algorithms, seven input-data schemes, and two validation settings on each annotated dataset. We also tested the obtained models using a majority-vote approach for classifying unseen patient records in the hold-out dataset.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>From the independently obtained bootstrap validation for <jats:italic>Dset A<\/jats:italic> and <jats:italic>Dset B<\/jats:italic>, mean accuracies across all seven data schemes were <jats:inline-formula><jats:alternatives><jats:tex-math>$$85.3\\%$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                    <mml:mrow>\n                      <mml:mn>85.3<\/mml:mn>\n                      <mml:mo>%<\/mml:mo>\n                    <\/mml:mrow>\n                  <\/mml:math><\/jats:alternatives><\/jats:inline-formula> (<jats:inline-formula><jats:alternatives><jats:tex-math>$$\\pm 9.2\\%$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                    <mml:mrow>\n                      <mml:mo>\u00b1<\/mml:mo>\n                      <mml:mn>9.2<\/mml:mn>\n                      <mml:mo>%<\/mml:mo>\n                    <\/mml:mrow>\n                  <\/mml:math><\/jats:alternatives><\/jats:inline-formula>) and <jats:inline-formula><jats:alternatives><jats:tex-math>$$97.1\\%$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                    <mml:mrow>\n                      <mml:mn>97.1<\/mml:mn>\n                      <mml:mo>%<\/mml:mo>\n                    <\/mml:mrow>\n                  <\/mml:math><\/jats:alternatives><\/jats:inline-formula> (<jats:inline-formula><jats:alternatives><jats:tex-math>$$\\pm 3.4\\%$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                    <mml:mrow>\n                      <mml:mo>\u00b1<\/mml:mo>\n                      <mml:mn>3.4<\/mml:mn>\n                      <mml:mo>%<\/mml:mo>\n                    <\/mml:mrow>\n                  <\/mml:math><\/jats:alternatives><\/jats:inline-formula>), respectively. Best accuracies were <jats:inline-formula><jats:alternatives><jats:tex-math>$$98.8\\%$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                    <mml:mrow>\n                      <mml:mn>98.8<\/mml:mn>\n                      <mml:mo>%<\/mml:mo>\n                    <\/mml:mrow>\n                  <\/mml:math><\/jats:alternatives><\/jats:inline-formula> and <jats:inline-formula><jats:alternatives><jats:tex-math>$$98.9\\%$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                    <mml:mrow>\n                      <mml:mn>98.9<\/mml:mn>\n                      <mml:mo>%<\/mml:mo>\n                    <\/mml:mrow>\n                  <\/mml:math><\/jats:alternatives><\/jats:inline-formula>. Both validation setting results were consistent. For the hold-out dataset, results were consonant with most of those obtained in the literature in terms of class proportions.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>The development of machine learning systems for the classification of diabetes subtypes constitutes an important task to support physicians for fast and timely decision-making. We expect to deploy this methodology in a data analysis platform to conduct studies for identifying T2DM subtypes in patient records from hospitals.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s13040-023-00340-2","type":"journal-article","created":{"date-parts":[[2023,8,22]],"date-time":"2023-08-22T04:02:48Z","timestamp":1692676968000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Machine learning based study for the classification of Type 2 diabetes mellitus subtypes"],"prefix":"10.1186","volume":"16","author":[{"given":"Nelson E.","family":"Ordo\u00f1ez-Guillen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jose\u00a0Luis","family":"Gonzalez-Compean","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ivan","family":"Lopez-Arevalo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Miguel","family":"Contreras-Murillo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Edwin","family":"Aldana-Bobadilla","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,8,22]]},"reference":[{"key":"340_CR1","unstructured":"International Diabetes Federation. 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