Gabin Drouard, Juha Mykkänen, Jarkko Heiskanen, Joona Pohjonen, Saku Ruohonen, Katja Pahkala, Terho Lehtimäki, Xiaoling Wang, Miina Ollikainen, Samuli Ripatti, Matti Pirinen, Olli Raitakari, Jaakko Kaprio
BACKGROUND: Machine learning (ML) classifiers are increasingly used for predicting cardiovascular disease (CVD) and related risk factors using omics data, although these outcomes often exhibit categorical nature and class imbalances. However, little is known about which ML classifier, omics data, or upstream dimension reduction strategy has the strongest influence on prediction quality in such settings. Our study aimed to illustrate and compare different machine learning strategies to predict CVD risk factors under different scenarios...
May 2, 2024: BMC Medical Informatics and Decision Making