G C A T T A C G G C A T genes Article Blood-Based Biomarkers for Predicting the Risk for Five-Year Incident Coronary Heart Disease in the Framingham Heart Study via Machine Learning Meeshanthini V. Dogan 1,2,3,*, Steven R. H. Beach 4, Ronald L. Simons 5, Amaury Lendasse 6,7, Brandan Penaluna 8 and Robert A. Philibert 1,2,3,8 1 Department of Biomedical Engineering, University of Iowa, Iowa City, IA 52242, USA;
[email protected] 2 Cardio Diagnostics LLC, 2500 Crosspark Road, Coralville, IA 52241, USA 3 Department of Psychiatry, University of Iowa, Iowa City, IA 52242, USA 4 Department of Psychology, University of Georgia, Athens, GA 30602, USA;
[email protected] 5 Department of Sociology, University of Georgia, Athens, GA 30606, USA;
[email protected] 6 Information and Logistics Technology Department, University of Houston, Houston, TX 77004, USA;
[email protected] 7 Department of Business Management and Analytics, Arcada University of Applied Sciences, 00560 Helsinki, Finland 8 Behavioral Diagnostics LLC, 2500 Crosspark Road, Coralville, IA 52241, USA;
[email protected] * Correspondence:
[email protected]; Tel.: +1-319-353-4986 Received: 15 November 2018; Accepted: 12 December 2018; Published: 18 December 2018 Abstract: An improved approach for predicting the risk for incident coronary heart disease (CHD) could lead to substantial improvements in cardiovascular health. Previously, we have shown that genetic and epigenetic loci could predict CHD status more sensitively than conventional risk factors. Herein, we examine whether similar machine learning approaches could be used to develop a similar panel for predicting incident CHD.