The Cost-Based Feature Selection Model for Coronary Heart Disease Diagnosis System Using Deep Neural Network

The development of feature selection models in intelligence systems for the diagnosis of coronary heart disease has been widely carried out. One of the developments that have been carried out is to minimize the number of inspections carried out. Unfortunately, many features selection models do not consider the cost of inspection, so the result of feature selection is an average inspection that requires high costs. This study proposes an intelligence system model for the diagnosis of coronary heart disease using a feature selection model that considers the cost of the examination. Feature selection is developed using a genetic algorithm and support vector machine. Decision-making of the diagnosis system is carried out using a deep neural network, with system performance being measured using the parameters of accuracy, sensitivity, positive predictive value, and area under the curve (AUC). The test results use the z-Alizadeh sani model feature selection dataset which produces 5 features out of 54 existing features. The use of these 5 features can produce AUC performance of 93.7%, accuracy of 87.7%, and sensitivity of 87.7%. Referring to the resulting performance, it shows that the feature selection model by considering the cost of an inspection can provide performance in the very good category.