Heart Disease Prediction Algorithm Based on Ensemble Learning

• Published in: 2020 7th International Conference on Dependable Systems and Their Applications (DSA) pp. 293 - 298
• Main Authors: Yuan, Ke, Yang, Longwei, Huang, Yabing, Li, Zheng
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-11-2020
• Subjects: Big Data; Big data analysis; Diseases; Heart; heart disease prediction; HGBDTLR algorithm; machine learning; Machine learning algorithms; Prediction algorithms; Software algorithms; Training
• DOI: 10.1109/DSA51864.2020.00052

Nowadays, heart disease is one of the important causes of human deaths. According to statistics, deaths caused by heart disease account for about one-third of all deaths in the world. With further research, the use of machine learning to predict heart disease has become an essential method to prevent and treat heart disease. In recent years, machine learning based on big data analysis has been widely used in various software applications, but it has not been used on a large scale in disease prediction. In this article, we propose a new algorithm named hybrid gradient boosting decision tree with logistic regression (HGBDTLR) based on ensemble learning to improve the accuracy of machine learning in heart disease prediction. The actual results prove that the prediction accuracy of HGBDTLR algorithm can reach 91.8% in the Cleveland heart disease data set.