Comparison between Neural Networks and Binary logistic Regression for Classification Observation (Case Study: risk factors for cardiovascular disease)

Abstract

the distinction between the artificial neural network method and the logistic regression method was discussed in this study as one of the methods suggested to be used in dual-data response. That is for preference between the two used methods, we used the proportion of misclassified observations, model accuracy and the area under the curved ROC as a criterion to compare between the two methods. Accordingly, This hospital-based case-control study involved 750 cardiovascular disease cases and 50 controls all recruited from Madani Heart Centre in Sudan, in 2019, The study aimed at knowing the most important risk factors for cardiovascular disease, and comparison between the Binary Logistic model and the Neural Networks models, also recognition of the best statistical approaches between the two methodologies for processing such data. To process the data, the study used the (SPSS) version 25. The main results that the study reached that the two used methods are similar regarding the significance of both the effect and the importance of the independent variables considered in the analysis, but the method of artificial neural networks gained a better classification proportion than the Binary Logistic Regression model. The most important recommendations of the study that making use of the statistical methods and generalizing the application of both Neural Networks and Logistic model in all fields of knowledge.