Prediction of mortality rates in heart failure patients with data mining methods

Abstract

Heart failure is one of the severe diseases which menace the human health and affect millions of people. Half of all patients diagnosed with heart failure die within four years. For the purpose of avoiding life-threatening situations and minimizing the costs, it is important to predict mortality rates of heart failure patients. As part of a HEIF-5 project, a data mining study was conducted aiming specifically at extracting new knowledge from a group of patients suffering from heart failure and using it for prediction of mortality rates. The methodology of knowledge discovery in databases is analyzed within the framework of home telemonitoring. Several data mining methods such as a Bayesian network method, a decision tree method, a neural network method and a nearest neighbour method are employed. The accuracy for the data mining methods from the point of view of avoiding life-threatening situations and minimizing the costs is discussed. It seems that the decision tree method achieves the best accuracy results and is also interpretable for the clinicians.

Multi‐view ensemble learning with empirical kernel for heart failure mortality prediction

“…Another example, in 2016, R. Scott Evans used automatic identification and prediction tools to help identify patients with high‐risk HF . In 2013, Jan Bohacik et al used data mining methods to predict mortality in patients with HF . There are also some machine learning methods used to provide personalized medical services for HF patients.…”