An Organized Method for Heart Failure Classification

• Published in: 2023 International Conference on Emerging Smart Computing and Informatics (ESCI) pp. 1 - 6
• Main Authors: Sachdeva, Ravi Kumar, Singh, Kritya Deo, Sharma, Saurabh, Bathla, Priyanka, Solanki, Vikas
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.03.2023
• Subjects: Decision Tree; Heart Failure; Random Forest; SVM; XGBoost
• DOI: 10.1109/ESCI56872.2023.10099809

Heart failure is a major health hazard, and millions all over the world are affected by this chronic syndrome each year. Predicting heart failure in patients is extremely tough and inaccurate. To improve the prediction success rates, the authors developed a machine-learning model with improved accuracy. In this paper, the authors have used the publicly available heart failure clinical records dataset, available on UCI repository, which contains medical particulars of 299 patients with heart failure. The authors have applied four machine learning classifiers: Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), and XGBoost to predict the survival chances of patients. SVM achieved the highest accuracy of 96.67% for patient survival prediction in comparison with all the other employed machine learning classification techniques.