Congestive Heart Failure Prediction Using Artificial Intelligence

One of the main reasons people for hospitalization of adults over the age of 65 is heart failure (HF) or congestive heart failure (CHF). CHF affects millions of people globally and is among the primary causes of death. Heart failure means the heart is doing less work (pumping less blood) than usual. It may be due to increased pressure in the heart, which gradually leave the heart too weak hence a slower rate of blood flowing through the body. Before it’s too late, heart failure can be prevented by analyzing and controlling the conditions that can cause it. In this work, we use Cardiovascular Health Study (CHS) dataset and compare five different machine learning techniques to predict congestive heart failure (CHF). For feature selection, we employ the decision tree (DT) C4.5 method and the Predictive Mean Matching (PMM) method for missing data imputation. From the different ways applied, our proposed method gives the optimal result.