Abstract
Today the most significant public health problem is Heart Failure (HF). There are a lot of raw medical data available to healthcare organizations in the form of structured and unstructured datasets, but the need is to analyze this data to get information and to make intelligent decisions. By using data mining, classification tool on a real dataset of cardiac patients we propose a model which classified these patients into four major classes. This model will help to identify the risk of HF and patients who have no HF signs but structural irregularities. We can also identify the patients having HF signs and irregularities and those having the critical stage of HF. This paper provides a detailed summary of modern strategies for management and analysis of HF patients by classes (1 to 4) that have appeared in the past few years.
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© 2016 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Saqlain, M., Liaqat, R.M., Saqib, N.A., Hameed, M. (2016). A Classification Model for Predicting Heart Failure in Cardiac Patients. In: Ahmed, M., Begum, S., Raad, W. (eds) Internet of Things Technologies for HealthCare. HealthyIoT 2016. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 187. Springer, Cham. https://doi.org/10.1007/978-3-319-51234-1_6
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DOI: https://doi.org/10.1007/978-3-319-51234-1_6
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