Dietary protein is the strong predictor of coronary artery disease; a data mining approach

Abstract Backgrounds and aims: Coronary artery disease (CAD) is the major cause of mortality and morbidity globally. Diet is known to contribute to CAD risk, and the dietary intake of specific macro- or micro-nutrients might be potential predictors of CAD risk. Machine learning methods may be helpful in the analysis of the contribution of several parameters in dietary including macro- and micro-nutrients to CAD risk. Here we aimed to determine the most important dietary factors for predicting CAD.Methods: Total 273 cases with more than 50% obstruction in at least one coronary artery and 443 healthy controls who completed a food frequency questionnaire (FFQ) were entered into the study. All dietary intakes were adjusted for energy intake. QUEST method was applied to determine the diagnosis pattern of CAD.Results: Total 34 dietary variables obtained from FFQ were entered the study that 23 of these variables were significantly associated with CAD according to t-test. Out of 23 dietary input variables adjusted protein, manganese, biotin, zinc and cholesterol remained in the model. According to our tree, only protein intake could identify the patients with coronary artery stenosis according to angiography from healthy participant up to 80%. Manganese dietary intake was the second important variable after protein. The accuracy of the tree was 84.36% for training dataset and 82.94% for testing dataset.Conclusion: Among different macro- and micro-nutrients in the dietary, a combination of protein, manganese, biotin, zinc and cholesterol could predict the presence of CAD.