Fuzzy rule-based system applied to risk estimation of cardiovascular patients

Cardiovascular decision support is one area of increasing research interest. On-going collaborations between clinicians and computer scientists are looking at the application of knowledge discovery in databases to the area of patient diagnosis, based on clinical records. A fuzzy rule-based system for risk estimation of cardiovascular patients is proposed. It uses a group of fuzzy rules as a knowledge representation about data pertaining to cardiovascular patients. Several algorithms for the discovery of an easily readable and understandable group of fuzzy rules are formalized and analysed. The accuracy of risk estimation and the interpretability of fuzzy rules are discussed. Our study shows, in comparison to other algorithms used in knowledge discovery, that classifcation with a group of fuzzy rules is a useful technique for risk estimation of cardiovascular patients. © 2013 Old City Publishing, Inc

Discretization for Naive Bayes Taking the Specifics of Heart Data into Account

At the present time heart disease is a major cause of death. Factors such as physical inactiveness, obesity, diabetes, social isolation and aging are expected to make the situation worse. It is worsened even further with misdiagnosis of patients describing heart related issues. A probability decision support approach to diagnosis of heart disease based on Naive Bayes is discussed here as most hospitals collect patient records but these are rarely used for automatic decision support. The approach is analyzed on Statlog heart data with the focus on improving preprocessing methods. As the result, a discretization algorithm with Equal Frequency Discretization which considers the specifics of engaged heart disease patients is presented. Enhancements of achieved accuracy with the added discretization and in comparison with other machine learning algorithms are shown in experiments founded on 10-fold cross-validation

Risk estimation of cardiovascular patients using Weka

Cardiovascular diseases remain the most prevalent cause of deaths worldwideand their prevention requires major life-style changes using limited health-care resources.Remote decision support for cardiovascular patients seems to allow them to lead a productivelife and to minimize the costs of treatment. In this paper, risk estimation of cardiovascularpatients on the basis of collected data used in our developing decision-makingsupport system is described. The system makes use of some data mining techniqueswhich are implemented in open source software tool Weka - Waikato Environment forKnowledge Analysis. The integration of Weka with our system, a description of used riskestimation models based on data mining techniques, and experimental results showing theperformance of these models are also given

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

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

Estimation of cardiovascular patient risk with a Bayesian network

Cardiovascular decision-making support experiences increasing research interest of scientists.Ongoing collaborations between clinicians and computer scientists are looking at the application of datamining techniques to the area of individual patient diagnosis, based on clinical records. An investigation of aBayesian network learnt according to a generated decision tree with cardiovascular data for estimation ofpatient risk in cardiovascular domains is presented. Promising experimental results are also provided