Analysis and Visualization of Heart Failure Prediction Dataset

This research work is based on the analysis of dataset of heart failure patients to predict their chances of survival. Cardiovascular diseases (CVDs) is the highest reason for deaths across the world. According to the statistics of 2019, 17.9 million people died due to heart failure which forms around 32% of the global deaths. Early prediction of the disease may act as a helping aid to medical professional to take required actions for curing the disease. For the same, several factors like serum creatinine, ejection fraction, age and medical history of the patient are considered. This information is used to evaluate the chances of survival of the patient. The analysis of the dataset is performed by pre-processing followed by statistical and visual analysis for different attributes. The experimental simulation is implemented in python. The major contribution of the research work is to draw inferences from statistical and visual analysis of the data so as to make the model easily understandable for a naïve user.