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 affectmillions of people. Half of all patients diagnosed with heart failure die within four years. For thepurpose of avoiding life-threatening situations and minimizing the costs, it is important to predictmortality rates of heart failure patients. As part of a HEIF-5 project, a data mining study wasconducted aiming specifically at extracting new knowledge from a group of patients suffering fromheart failure and using it for prediction of mortality rates. The methodology of knowledge discoveryin databases is analyzed within the framework of home telemonitoring. Several data mining methodssuch as a Bayesian network method, a decision tree method, a neural network method and a nearestneighbour method are employed. The accuracy for the data mining methods from the point of view ofavoiding life-threatening situations and minimizing the costs is discussed. It seems that the decisiontree method achieves the best accuracy results and is also interpretable for the clinicians