Abstract
INTRODUCTION: Heart disease remains one of the leading causes of mortality worldwide, necessitating the development of accurate and efficient prediction models
OBJECTIVES: To research new models for heart disease prediction
METHODS: This paper presents a novel approach for predicting heart disease using advanced artificial intelligence (AI) techniques, including machine learning (ML) and deep learning (DL) algorithms
RESULTS By leveraging patient data and integrating various AI models, this approach aims to enhance prediction accuracy and support early diagnosis and intervention
CONCLUSION: This study presents a novel AI-based approach for heart disease prediction, demonstrating the efficacy of ML and DL models in improving diagnostic accuracy
Publisher
European Alliance for Innovation n.o.
Reference14 articles.
1. [1] World Health Statistics. Cardiovascular Diseases, Key Facts. 2021. Available online:room/factsheets/detail/cardiovascular-diseases-(cvds) (accessed on 10 December 2022).
2. [2] Choudhury, R.P.; Akbar, N. Beyond Diabetes: A Relationship between Cardiovascular Outcomes and Glycaemic Index. Cardiovasc.Res. 2021, 117, E97–E98.
3. [3] Magesh, G.; Swarnalatha, P. Optimal Feature Selection through a Cluster-Based DT Learning (CDTL) in Heart Disease Prediction.Evol. Intell. 2021, 14, 583–593.
4. [4] Rohit Chowdary, K.; Bhargav, P.; Nikhil, N.; Varun, K.; Jayanthi, D. Early Heart Disease Prediction Using Ensemble LearningTechniques. J. Phys. Conf. Ser. 2022, 2325, 012051.
5. [5] Liu, J.; Dong, X.; Zhao, H.; Tian, Y. Predictive Classifier for Cardiovascular Disease Based on Stacking Model Fusion. Processes2022, 10, 749.