Modeling the diagnosis of coronary artery disease by discriminant analysis and logistic regression: a cross-sectional study

Abstract

Purpose Coronary artery disease (CAD) is one of the most significant cardiovascular diseases that requires accurate angiography to diagnose. Angiography is an invasive approach involving risks like death, heart attack, and stroke. An appropriate alternative for diagnosis of the disease is to use statistical or data mining methods. The purpose of the study was to predict CAD by using discriminant analysis and compared with the logistic regression. Materials and methods This cross-sectional study included 758 cases admitted to Fatemeh Zahra Teaching Hospital (Sari, Iran) for examination and coronary angiography for evaluation of CAD in 2019. A logistics discriminant, Quadratic Discriminant Analysis (QDA) and Linear Discriminant Analysis (LDA) model and K-Nearest Neighbor (KNN) were fitted for prognosis of CAD with the help of clinical and laboratory information of patients. Results Out of the 758 examined cases, 250 (32.98%) cases were non-CAD and 508 (67.22%) were diagnosed with CAD disease. The results indicated that the indices of accuracy, sensitivity, specificity and area under the ROC curve (AUC) in the linear discriminant analysis (LDA) were 78.6, 81.3, 71.3, and 81.9%, respectively. The results obtained by the quadratic discriminant analysis were respectively 64.6, 88.2, 47.9, and 81%. The values of the metrics in K-nearest neighbor method were 74, 77.5, 63.7, and 82%, respectively. Finally, the logistic regression reached 77, 87.6, 55.6, and 82%, respectively for the evaluation metrics. Conclusions The LDA method is superior to the Quadratic Discriminant Analysis (QDA), K-Nearest Neighbor (KNN) and Logistic Regression (LR) methods in differentiating CAD patients. Therefore, in addition to common non-invasive diagnostic methods, LDA technique is recommended as a predictive model with acceptable accuracy, sensitivity, and specificity for the diagnosis of CAD. However, given that the differences between the models are small, it is recommended to use each model to predict CAD disease.

Comparing the performances of support vector machines, artificial neural network, and a logistic regression model for predicting coronary artery diseases: a cross‑sectional study

“…The main variable in this study has been the presence or absence of coronary artery disease based on the cardiologists' opinion a well as the results of angiography. A previously published article study [21] has explained how to calculate the sample size, the criteria for diagnosing coronary artery disease as well as the demographic, clinical and laboratory variables, and the inclusion criteria for selected patients.…”

“…After angiography, 250 (32.98%) subjects came up to be non-CAD and 508 (67.02%) subjects were diagnosed with CAD. The details of baseline characteristics of non-CAD and CAD cases are presented in the study of Shariatnia et al [21].…”

Risk factors for high CAD-RADS scoring in CAD patients revealed by machine learning methods: a retrospective study

“…The pathogenesis of CAD is complex and is affected by a variety of risk factors, with atherosclerosis being the most common underlying cause of cardiovascular diseases ( Popa et al., 2020 ). Multiple conventional risk factors augment the atherosclerotic process, including age,sex,smoking, hypertension, hyperlipidemia, DM, hyperuricemia, coagulation abnormalities, obesity, insulin resistance, C-reactive protein levels, plasma fibrinogen, and others ( Giacco & Brownlee, 2010 ; Kazemian et al., 2020 ; Shariatnia et al., 2022 ; Song et al., 2015 ; Tsai, Chiang & Huang, 2020 ; Velusamy & Ramasamy, 2021 ; Williams et al., 2018 ; Yang et al., 2018 ). It is indispensable to comprehend and properly calculate the etiological contribution of these risk factors to devise and improve preventive tactics for CAD.…”

“…These algorithms have emerged as valuable tools for predicting patient outcomes based on pertinent feature characteristics variables and have already been applied to identify unknown CAD risk factors, automate imaging interpretation, and enhance clinical decision-making, thus facilitating precision medicine ( Huang et al., 2022 ; Panteris et al., 2022 ; Saravi et al., 2022 ). Some of the most widely used mathematical methods for predictions are discriminant analysis, logistic regression, neural networks, and classification and regression trees ( Shariatnia et al., 2022 ). The strongest predictors can be selected to train the system to predict outcomes using supervised learning ( Khalaji et al., 2022 ).…”

Intelligent prediction of major adverse cardiovascular events (MACCE) following percutaneous coronary intervention using ANFIS-PSO model

“…It is estimated that more than 23.6 million deaths in various communities will be due to cardiovascular disease [ 1 ]. CAD is the first and most common cause of death in Iranians of all ages [ 2 ].…”