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Predicting Heart Disease by Means of Associative Classification


Hisham Ogbah, Abdallah Alashqur, Hazem Qattous


Vol. 16  No. 9  pp. 24-32


In this age of “Internet of Things” and “Smart Cities” the importance of the field of data mining has increased significantly. Data mining is needed because of the huge amounts of digital data that is being generated globally on an hourly basis (referred to as Big Data). Data mining provides methods and techniques to analyze large repositories or fast-moving streams of data at a massive scale. The data mining process extracts useful information and knowledge, from big data, that benefit the decision maker. Classification is one of the data mining techniques used to analyze existing data and predict classes of new data. Association rule mining is another data mining technique that is used to extract frequent patters and association rules relating pieces of data together. A distinguished classification technique called associative classification combines classification and association rule mining to predict class labels of data. Health care is one of the areas in which data mining is extremely useful. Mining medical and patient data can help in discovering patients’ conditions and aid in the diagnosis process. In this paper we show how associative classification is applied in the prediction of heart disease, which is one of the most common types of diseases that affect people’s lives.


Data mining, Association rules mining, Associative classification, Mining health care data