An Organized Method for Heart Failure Classification
Ravi Kumar Sachdeva, Kritya Deo Singh, Saurabh Sharma, Priyanka Bathla, Vikas Solanki
Abstract
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.