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Classification rules are an important tool for discovering knowledge from databases. Integrating fuzzy logic algorithms into databases allows us to reduce uncertainty which is connected with data in databases and to increase discovered knowledge's accuracy. In this paper, we analyze some possible variants of making classification rules from a given fuzzy(More)
Cardiovascular decision support is one area of increasing research interest. Ongoing 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(More)
About 50% of the patients diagnosed with heart failure die within four years. At the same time, a rise in home telemonitoring of these patients can be observed. For its successful deployment, predicting if a heart failure patient could die within a certain period of time is an important task. An investigation of an alternating decision tree employed for(More)
— Cardiovascular disease is the principal cause of death in most European countries and may have a major negative impact on the patients' functional status, productivity, and quality of life. It seems an automatic decision support system could lower these negative impacts. The current development stage of a patient-centric solution for remote management and(More)
Real-world data containing instances corresponding to patients with otoneurological diseases were explored with fuzzy IF-THEN rule induction. It was based on transformation of a fuzzy decision tree made with using cumulative information estimations as the locally optimal criterion at its nodes. This method uses linguistic variables that allow us to(More)
—The prevalence of heart failure is 2-3% of the adult population and it is expected to grow. Half of all patients diagnosed with it die within four years. To minimize life-threatening situations and to minimize costs, it is interesting to predict mortality rates for a patient with heart failure. In this paper, a fuzzy decision tree based on classification(More)