Predicting Heart Failure Disease Using Machine Learning

Heart Failure (HF) is a common health condition that affects more than 600,000 American every year and results in their death. Luckily, machine learning classification, regression and prediction models are key approaches and techniques that can be used to detect and predict the cases of heart disease or failure. The study included in this paper based on a dataset that contains 918 instances or rows of various medical records. This research paper attempts to use these medical records to improve Heart failure disease prediction accuracy. For that, Multiple popular Machine Learning models were used to understand the data and provide a better prediction and results, based on different evaluation metrics. Furthermore, the results section in this study shows a better accuracy score compared with other related work using different Machine learning algorithms and software. Finally, RStudio and Weka software are used in this paper to perform some of the algorithms and the best model results were using the Random Forest and Logistic Regression algorithms. These tools assisted us in better understanding of the data and data preprocessing.