{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T05:50:10Z","timestamp":1790315410580,"version":"4.1.0"},"reference-count":58,"publisher":"Maad Rayan Publishing Company","issue":"10","content-domain":{"domain":["journalaim.com"],"crossmark-restriction":false},"short-container-title":["Arch Iran Med"],"published-print":{"date-parts":[[2024,10,1]]},"abstract":"<jats:p>Background: Metabolic dysfunction-associated steatotic liver disease (MASLD) represents a significant global health burden without established curative therapies. Early detection and preventive strategies are crucial for effective MASLD management. This study aimed to develop and validate machine-learning (ML) algorithms for accurate MASLD screening in a geographically diverse, large-scale population. Methods: Data from the prospective Fasa Cohort Study, initiated in rural Fars province, Iran (March 2014), were employed for this purpose. The required data were collected using blood tests, questionnaires, liver ultrasonography, and physical examinations. A two-step approach identified key predictors from over 100 variables: (1) statistical selection using mean decrease Gini in random forest and (2) incorporation of clinical expertise for alignment with known MASLD risk factors. The hold-out validation approach (with a 70\/30 train\/validation split) was utilized, along with 5-fold cross-validation on the validation set. Logistic regression, Na\u00efve Bayes, support vector machine, and light gradient-boosting machine (LightGBM) algorithms were compared for model construction with the same input variables based on area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy. Results: A total of 6,180 adults (52.7% female) were included in the study, categorized into 4816 non-MASLD and 1364 MASLD cases with a mean age (\u00b1standard deviation [SD]) of 48.12 (\u00b19.61) and 49.47 (\u00b19.15) years, respectively. Logistic regression outperformed other ML algorithms, achieving an accuracy of 0.88 (95% confidence interval [CI]: 0.86-0.89) and an AUC of 0.92 (95% CI: 0.90-0.93). Among more than 100 variables, the key predictors included waist circumference, body mass index (BMI), hip circumference, wrist circumference, alanine aminotransferase levels, cholesterol, glucose, high-density lipoprotein, and blood pressure. Conclusion: Integration of ML in MASLD management holds significant promise, particularly in resource-limited rural settings. Additionally, the relative importance assigned to each predictor, particularly prominent contributors such as waist circumference and BMI, offers valuable insights into MASLD prevention, diagnosis, and treatment strategies.<\/jats:p>","DOI":"10.34172\/aim.31269","type":"journal-article","created":{"date-parts":[[2024,10,13]],"date-time":"2024-10-13T05:16:47Z","timestamp":1728796607000},"page":"551-562","update-policy":"https:\/\/doi.org\/10.34172\/crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Machine-Learning Application for Predicting Metabolic Dysfunction-Associated Steatotic Liver Disease Using Laboratory and Body Composition Indicators"],"prefix":"10.34172","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8439-5024","authenticated-orcid":true,"given":"Fatemeh","family":"Masaebi","sequence":"first","affiliation":[{"name":"Department of Biostatistics, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mehdi","family":"Azizmohammad Looha","sequence":"additional","affiliation":[{"name":"Basic and Molecular Epidemiology of Gastrointestinal Disorders Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Morteza","family":"Mohammadzadeh","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, School of Public Health, Iran University of Medical Sciences, Tehran, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vida","family":"Pahlevani","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, Faculty of Medical Science, Tarbiat Modares University, Tehran, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mojtaba","family":"Farjam","sequence":"additional","affiliation":[{"name":"Noncommunicable Diseases Research Center, Fasa University of Medical Sciences, Fasa, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7791-8122","authenticated-orcid":true,"given":"Farid","family":"Zayeri","sequence":"additional","affiliation":[{"name":"Proteomics Research Center and Department of Biostatistics, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5398-9519","authenticated-orcid":true,"given":"Reza","family":"Homayounfar","sequence":"additional","affiliation":[{"name":"National Nutrition and Food Technology Research Institute, Shahid Beheshti University of Medical Sciences, Tehran, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"20123","published-online":{"date-parts":[[2024,10,1]]},"reference":[{"key":"R1","doi-asserted-by":"publisher","DOI":"10.1038\/nrgastro.2013.171"},{"key":"R2","doi-asserted-by":"publisher","DOI":"10.1053\/gast.2001.23256"},{"key":"R3","doi-asserted-by":"publisher","DOI":"10.18502\/ijph.v52i8.13399"},{"key":"R4","doi-asserted-by":"publisher","DOI":"10.3350\/cmh.2022.0365"},{"key":"R5","doi-asserted-by":"publisher","DOI":"10.1016\/j.ando.2020.07.1109"},{"key":"R6","doi-asserted-by":"publisher","DOI":"10.3350\/cmh.2022.0239"},{"key":"R7","doi-asserted-by":"publisher","DOI":"10.2337\/dc10-0987"},{"key":"R8","doi-asserted-by":"publisher","DOI":"10.2337\/db22-0612"},{"key":"R9","doi-asserted-by":"publisher","DOI":"10.1186\/s44167-023-00015-7"},{"key":"R10","doi-asserted-by":"publisher","DOI":"10.1016\/j.clnesp.2022.06.001"},{"key":"R11","doi-asserted-by":"publisher","DOI":"10.1186\/s13098-021-00778-6"},{"key":"R12","doi-asserted-by":"publisher","DOI":"10.1161\/circulationaha.117.030898"},{"key":"R13","unstructured":"Lala V, Zubair M, Minter DA. 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