An accurate diagnosis of coronary heart disease by Catboost, with easily accessible data

Abstract

Abstract Coronary heart diseases (CHD) have become the leading cause of death worldwide. Coronary angiography is the “gold standard” for diagnosing this disease. However, the invasive risk and expensive price make it difficult to promote on a large scale. This study was using Catboost to diagnose CHD through simple indicators. 2642 samples, including 717 patients, were collected from 2018 to 2019. 33 features were collected, including demography, anthropometry, questionnaire and laboratory examination indicators. The diagnosis model of CHD was established by using Catboost, random forest and logistic regression. Accuracy and area under ROC (AUROC) were used to evaluate the classification performance of the diagnosis models. In order to facilitate the application, we also set up a simplified model merely based on non-laboratory dataset. Catboost showed the best performance in identifying patients with CHD. The accuracy of Catboost, random forest and logistic regression was 82.5%, 75.1%, 75.8%, respectively, and the AUROC of them was 0.881, 0.837, 0.832, respectively. Age, total cholesterol and family history of coronary heart disease were the three most important risk factors for diagnosing CHD. Catboost also worked best in simplified models with 77.9% accuracy and 0.857 AUROC. The models can contribute to early screening and diagnosis for CHD, which would facilitate the prevention and timely treatment of the diseases.