Heart failure survival prediction using machine learning algorithm: am I safe from heart failure?

Heart Failure (HF) is a prevalent ailment worldwide, and despite significant medical advancements in the past few decades, cardiovascular disease is still the leading cause of death. Although HF itself is a critical risk for patient survival, other co-existing pathophysiological conditions can present a significant threat to patient survival. Because so many elements contribute to a patient's survival in heart failure, predicting the chances of survival without using a computational technique can be difficult for cardiac doctors, eventually preventing the patient from receiving correct care. Fortunately, categorization and prediction models exist, which can assist cardiologists in designing proper treatment schemes using relevant medical data. This study aims to develop prediction models for patient survival in HF conditions. In this paper, we analyzed the UCI heart failure dataset containing relevant medical information of 299 HF patients. We applied several machine learning classifiers to predict the patient survival from HF-related pathophysiological parameters and analyzed the features corresponding to the most crucial risk factors using the correlation matrix. Our prediction models used the following machine learning techniques- Logistic Regression, Decision Tree, Support Vector Machine, XGBoost, LightGBM, Random Forest, KNN, and Bagging and were able to find a better result. Also, this paper presents a comparative study by analyzing the performance of different machine learning algorithms. Our analysis indicates that LightGBM achieved the highest Accuracy of 85% and AUC of 93% in predicting patient survival of HF patients compared to other machine learning algorithms.