Machine Learning Methods for Identifying Atrial Fibrillation Cases and Their Predictors in Patients With Hypertrophic Cardiomyopathy: The HCM-AF-Risk Model

no code implementations • 19 Sep 2021 • Moumita Bhattacharya, Dai-Yin Lu, Ioannis Ventoulis, Gabriela V. Greenland, Hulya Yalcin, Yufan Guan, Joseph E. Marine, Jeffrey E. Olgin, Stefan L. Zimmerman, Theodore P. Abraham, M. Roselle Abraham, Hagit Shatkay

Specifically, an ensemble of logistic regression and naive Bayes classifiers, trained based on the 18 variables and corrected for data imbalance, proved most effective for separating AF from No-AF cases (sensitivity = 0. 74, specificity = 0. 70, C-index = 0. 80).

Specificity