Comparing performances of logistic regression, classification and regression tree, and neural networks for predicting coronary artery disease

By I. Kurt, Mevlut Ture, A. Kurum. Expert systems with applications, 2008

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Machine learning in critical care: state-of-the-art and a sepsis case study (2018, methodology): “In the experiments reported here [84], the prognostic factors obtained by FA were used to predict mortality using Logistic Regression (LR), a method that is commonly employed in medical applications [85, 86] and widely trusted by clinicians.”
Data mining methods in the prediction of Dementia: A real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forests (2011, background): “Several authors propose that data mining classifiers have higher accuracy and lower error rates than the traditional classification methods [22,25,28,29].”
Mining electronic health records: towards better research applications and clinical care (2012, background): “Nature Reviews | Genetics Examples include the prediction of surgery outcome 53 , breast cancer survival 54 and coronary heart disease risk 55 from variables such as age, sex, smoking A simplified illustration of an electronic health record (EHR) research database and some of the data-driven…”
Unobtrusive Sensing and Wearable Devices for Health Informatics (2014, methodology): “A comparison has been made among the three different classiﬁcation methods in terms of their performance in predicting coronary artery disease [147].”