Hybridization of swarm for features selection to modeling heart attack data

Abstract EN

Predicting heart attacks using machine learning is an important topic.

Medical data sets contain different features, some of which are related to the target group for prediction and some are not.

In addition, the data sets are excessively unbalanced, which leads to the bias of machine learning models when modeling heart attacks.

To model the unbalanced heart attack data set, this paper proposes the hybridization of Particle swarm optimization (PSO), BAT, and Cuckoo Search (CS) to select the features and adopt the precision for minority classes as a fitness function for each swarm to select the influential features.

In order to model the data, set in which the features were selected, it was proposed to use the boosting (Catboost) as a classifier for predicting heart attacks.

The proposed method to select features has been compared with each of the three swarms, and the Catboost algorithm has been compared to traditional classification algorithms (naive Bayes, decision trees).

The study found that the proposed method of hybridization of the results of the (PSO, BAT, and BCS) algorithms in selecting features is a promising solution in the field of selecting features and increases the accuracy of the system, and that traditional machine learning models are biased in the case of unbalanced data sets and that selecting the important features according to the target class has an impact on the performance of the models, In addition, the definition of hyperparameters reduces the bias of the selected model.

The final model achieved an overall accuracy of 96% on the Accuracy scale and 56% the Precision scale for minority class on the