{"id":970,"date":"2026-01-16T11:15:16","date_gmt":"2026-01-16T10:15:16","guid":{"rendered":"https:\/\/cardiateam.eu\/?p=970"},"modified":"2026-01-16T11:22:11","modified_gmt":"2026-01-16T10:22:11","slug":"prediction-of-myocardial-infarction-using-a-combined-generative-adversarial-network-model-and-feature-enhanced-loss-function","status":"publish","type":"post","link":"https:\/\/cardiateam.eu\/index.php\/2026\/01\/16\/prediction-of-myocardial-infarction-using-a-combined-generative-adversarial-network-model-and-feature-enhanced-loss-function\/","title":{"rendered":"Prediction of Myocardial Infarction Using a Combined Generative Adversarial Network Model and Feature-Enhanced Loss Function"},"content":{"rendered":"<p><strong>Authors:<\/strong><\/p>\n<p>Shixiang Yu, Siyu Han, Mengya Shi, Makoto Harada, Jianhong Ge, Xuening, Xiang Cai, Margit Heier, Gabi Karstenm\u00fcller, Karsten Suhre, Christian Gieger, Wolfgang Koenig, Wolfgang Rathmann, Annette Peters and Rui Wang-Sattler<\/p>\n<p>&nbsp;<\/p>\n<p>Metabolites 2024, 14, 258.<\/p>\n<p>doi: https:\/\/doi.org\/10.3390\/ metabo14050258<\/p>\n<p>Published: 30 April 2024<\/p>\n<p>&nbsp;<\/p>\n<p><strong>Abstract:<\/strong><\/p>\n<p class=\"c-article__sub-heading\" data-test=\"abstract-sub-heading\">Accurate risk prediction for myocardial infarction (MI) is crucial for preventive strategies,<br \/>\ngiven its significant impact on global mortality and morbidity. Here, we propose a novel deep-learning<br \/>\napproach to enhance the prediction of incident MI cases by incorporating metabolomics alongside<br \/>\nclinical risk factors. We utilized data from the KORA cohort, including the baseline S4 and follow-up<br \/>\nF4 studies, consisting of 1454 participants without prior history of MI. The dataset comprised 19<br \/>\nclinical variables and 363 metabolites. Due to the imbalanced nature of the dataset (78 observed MI<br \/>\ncases and 1376 non-MI individuals), we employed a generative adversarial network (GAN) model<br \/>\nto generate new incident cases, augmenting the dataset and improving feature representation. To<br \/>\npredict MI, we further utilized multi-layer perceptron (MLP) models in conjunction with the synthetic<br \/>\nminority oversampling technique (SMOTE) and edited nearest neighbor (ENN) methods to address<br \/>\noverfitting and underfitting issues, particularly when dealing with imbalanced datasets. To enhance<br \/>\nprediction accuracy, we propose a novel GAN for feature-enhanced (GFE) loss function. The GFE loss<br \/>\nfunction resulted in an approximate 2% improvement in prediction accuracy, yielding a final accuracy<br \/>\nof 70%. Furthermore, we evaluated the contribution of each clinical variable and metabolite to the<br \/>\npredictive model and identified the 10 most significant variables, including glucose tolerance, sex, and<br \/>\nphysical activity. This is the first study to construct a deep-learning approach for producing 7-year<br \/>\nMI predictions using the newly proposed loss function. Our findings demonstrate the promising<br \/>\npotential of our technique in identifying novel biomarkers for MI prediction.<\/p>\n<p><a href=\"https:\/\/www.mdpi.com\/2218-1989\/14\/5\/258\" target=\"_blank\" rel=\"noopener\"><br \/>\nRead full publication<br \/>\n<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Authors: Shixiang Yu, Siyu Han, Mengya Shi, Makoto Harada, Jianhong Ge, Xuening, Xiang Cai, Margit Heier, Gabi Karstenm\u00fcller, Karsten Suhre, Christian Gieger, Wolfgang Koenig, Wolfgang Rathmann, Annette Peters and Rui Wang-Sattler &nbsp; Metabolites 2024, 14, 258. doi: https:\/\/doi.org\/10.3390\/ metabo14050258 Published: 30 April 2024 &nbsp; Abstract: Accurate risk prediction for myocardial infarction (MI) is crucial for&hellip;<\/p>\n","protected":false},"author":1,"featured_media":971,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_themeisle_gutenberg_block_has_review":false,"footnotes":""},"categories":[8],"tags":[],"class_list":["post-970","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-publications"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v25.4 - 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