Predicting the likelihood of heart failure with a multi level risk assessment using decision tree
- 1 April 2015
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
Abstract
Heart failure comes in the top causes of death worldwide. The number of deaths from heart failure exceeds the number of deaths resulting from any other causes. Recent studies have focused on the use of machine learning techniques to develop predictive models that are able to predict the incidence of heart failure. The majority of these studies have used a binary output class, in which the prediction would be either the presence or absence of heart failure. In this study, a multi-level risk assessment of developing heart failure has been proposed, in which a five risk levels of heart failure can be predicted using C4.5 decision tree classifier. On the other hand, we are boosting the early prediction of heart failure through involving three main risk factors with the heart failure data set. Our predictive model shows an improvement on existing studies with 86.5% sensitivity, 95.5% specificity, and 86.53% accuracy.Keywords
This publication has 10 references indexed in Scilit:
- Alternating decision trees for early diagnosis of heart diseasePublished by Institute of Electrical and Electronics Engineers (IEEE) ,2014
- An ensemble based decision support framework for intelligent heart disease diagnosisPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2014
- A Systematic Comparison of Supervised ClassifiersPLOS ONE, 2014
- Machine learning, medical diagnosis, and biomedical engineering research - commentaryBioMedical Engineering OnLine, 2014
- Sensitivity and specificity of machine learning classifiers for glaucoma diagnosis using Spectral Domain OCT and standard automated perimetryArquivos Brasileiros de Oftalmologia, 2013
- Classification Tree for Risk Assessment in Patients Suffering From Congestive Heart Failure via Long-Term Heart Rate VariabilityIEEE Journal of Biomedical and Health Informatics, 2013
- A Heart Disease Prediction Model using Decision TreeIOSR Journal of Computer Engineering, 2013
- Prediction and decision making in Health Care using Data MiningInternational Journal of Public Health Science (IJPHS), 2012
- Early Detection of Clinical Parameters in Heart Disease by Improved Decision Tree AlgorithmPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2010
- The Heart Failure EpidemicInternational Journal of Environmental Research and Public Health, 2010