Prediction of Mortality from Heart Failure using Machine Learning

Abstract

Cardiovascular diseases (CVDs) or heart failure (HF) is a vital cause of death worldwide. Approximately 17.9 million people die each year, and it accounts for 31% of global deaths in India as well as across the world. HF has become one of the most life-threatening diseases that is commonly caused by CVDs. When treating patients, it is critical in medicine to make the proper judgments in a short amount of time. Given the large quantity of data generated by the healthcare sector, ML technologies play an essential role in predicting CVD. In this work, various ML-based algorithms are applied to a heart disease dataset to predict the mortality of patients from HF. A comparative analysis of the models based upon various performance measures (accuracy, precision, and recall) has been done. Finally, ensemble learning is used for the prediction of mortality from HF with an accuracy of 90%, -achieved on the test data.

Ähnliche Arbeiten