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Modeling the diagnosis of coronary artery disease by discriminant analysis and logistic regression: a cross-sectional
Sahar Shariatnia1, Majid Ziaratban2, Abdolhalim Rajabi3
1Department of Biostatistics and Epidemiology, Faculty of Health, Golestan University of Medica Science, Gorgan, Iran.
Insights
Linear Discriminant Analysis (LDA) shows promise in predicting Coronary Artery Disease (CAD) by outperforming other statistical models. This data mining approach offers an accurate, sensitive, and specific alternative to invasive diagnostic methods for CAD.
Area of Science:
- Cardiovascular Medicine
- Medical Informatics
- Statistical Modeling
Background:
- Coronary Artery Disease (CAD) is a leading cause of cardiovascular mortality.
- Traditional diagnosis via angiography is invasive and carries significant risks.
- There is a need for accurate, non-invasive diagnostic alternatives for CAD.
Purpose of the Study:
- To predict Coronary Artery Disease (CAD) using discriminant analysis.
- To compare the predictive performance of Linear Discriminant Analysis (LDA) against Logistic Regression, Quadratic Discriminant Analysis (QDA), and K-Nearest Neighbor (KNN).
Main Methods:
- A cross-sectional study of 758 patients undergoing coronary angiography was conducted.
- Clinical and laboratory data were used to train and evaluate LDA, QDA, KNN, and Logistic Regression models.
- Model performance was assessed using accuracy, sensitivity, specificity, and Area Under the ROC Curve (AUC).
Main Results:
- Linear Discriminant Analysis (LDA) achieved the highest accuracy (78.6%), sensitivity (81.3%), and specificity (71.3%) among the evaluated models.
- All models demonstrated comparable Area Under the ROC Curve (AUC) values, ranging from 81% to 82%.
- Logistic Regression showed high sensitivity (87.6%) but lower specificity (55.6%).
Conclusions:
- Linear Discriminant Analysis (LDA) is a superior method for differentiating patients with Coronary Artery Disease (CAD) compared to QDA, KNN, and Logistic Regression.
- LDA presents a viable, accurate, sensitive, and specific predictive model for CAD diagnosis, complementing non-invasive methods.
- Given the small performance differences, utilizing multiple predictive models for CAD is recommended.
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.
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