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In recent time, heart disease has become common leading to mortality of many individuals. Hence, early and accurate prediction of this disease is vital to reduce death rate and enhance people's lives. Concurrently, Artificial Intelligence has gained more attention at present as it permits deeper understanding of the healthcare data thereby providing accurate prediction results. This efficient prediction will solve complicated queries regarding heart diseases and hence assists clinical practitioners to adopt smart medical decisions. Hence, this study intends to predict heart disease with high accuracy by proposing an improved feature selection and enhanced classification approach. The paper employs Grey-wolf with Firefly algorithm for effective feature selection and using Differential Evolution Algorithm for tuning the hyper parameters of Artificial Neural Network (ANN). Hence, it is named as Grey Wolf Firefly algorithm with Differential Evolution (GF-DE) for better classification of the selected features. This proposed classification model trains the neural network to obtain optimal weights and tunes huge number of hyper parameters in an efficiently. To prove this, the proposed system is comparatively analysed with existing methods in terms of performance metrics like accuracy, precision, recall and F1 score for Cleveland and Statlog dataset. In addition, statistical analysis is also undertaken to analyse the significance of proposed system. Outcomes revealed the efficiency of proposed method which makes it highly suitable for heart disease prediction in an efficient manner. [ABSTRACT FROM AUTHOR]

Diseases is an unusual circumstance that affects single or more parts of a human's body. Because of lifestyle and patrimonial, different kinds of disease are increasing day by day. Among all those diseases, heart disease turns out to be the most common disease and the impact of this ailment is dangerous than all other diseases. In this paper, we compared a number of computational intelligence techniques for the prediction of coronary artery heart disease. Seven computational intelligence techniques named as Logistic Regression (LR), Support Vector Machine (SVM), Deep Neural Network (DNN), Decision Tree (DT), Naïve Bayes (NB), Random Forest (RF), and K-Nearest Neighbor (K-NN) were applied and a comparative study was drawn. The performance of each technique was evaluated using Statlog and Cleveland heart disease dataset which are retrieved from the UCI machine learning repository database with several evaluation techniques. From the study, it can be carried out that the highest accuracy of 98.15% obtained by deep neural network with sensitivity and precision 98.67% and 98.01% respectively. The outcomes of the study were compared with the outcomes of the state of the art focusing on heart disease prediction that outperforms the previous study. [ABSTRACT FROM AUTHOR]