{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T11:34:22Z","timestamp":1778758462904,"version":"3.51.4"},"reference-count":37,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2022,10,12]],"date-time":"2022-10-12T00:00:00Z","timestamp":1665532800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Cardiovasc. Med."],"abstract":"<jats:sec><jats:title>Objective<\/jats:title><jats:p>Rheumatic heart disease (RHD) is a major health problem in the world, particularly in developing countries. This study aimed to predict mitral regurgitation (MR) and mitral stenosis (MS) RHD among children with RHD.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methodology<\/jats:title><jats:p>Data was collected from the Pediatric Cardiology Department at Chaudhry Pervaiz Elahi Institute of Cardiology Multan, Pakistan from March to October 2019. A sample of 561 children aged 4\u201314 years, who were diagnosed with RHD of either MR or MS, were recruited from the hospital\u2019s outpatient department. The presence of multivariate outliers was detected, and different machine learning methods, including subset logistic regression, subset logistic regression after deletion, stepwise winsorized logistic regression, robust logistic regression, subset deep neural network, and random forest models were compared using the area under receiver operating characteristics (ROC) curve, sensitivity, and specificity. Parsimony was also considered in model selection.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Out of 561 patients in this study, 75.94% had RHD MR and 24.06% had RHD MS. The average age of study participants was 9.19 \u00b1 2.45 years and of them 55.43% were male. Among the male participants, 58.6 and 45.2% had MR and MS, respectively; and among female participants, those were 70.4 and 29.6%, respectively. Subset logistic regression after deletion appeared as competitive with a discrimination power of 90.1% [95% CI 0.818\u20130.983]. The sensitivity and specificity of this model were 85.1 and 70.6%.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The best predictive model was subset logistic regression after deletion. The predicted method will be used in the decision-making process, which helps early diagnosis of the disease and leads to prevention. The study findings provide the proper guideline for earlier diagnosis of the RHD MR and MS cases among children with RHD in Pakistan.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fcvm.2022.996225","type":"journal-article","created":{"date-parts":[[2022,10,12]],"date-time":"2022-10-12T04:40:35Z","timestamp":1665549635000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":11,"title":["Machine learning methods for predicting major types of rheumatic heart diseases in children of Southern Punjab, 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