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Title:
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SEQUENCE PREDICTION ALGORITHM FOR HEART FAILURE PREDICTION |
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Author(s):
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Carine Bou Rjeily, Georges Badr, Amir Hajjam El Hassani and Emmanuel Andres |
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ISBN:
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978-989-8533-65-4 |
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Editors:
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Mário Macedo |
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Year:
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2017 |
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Edition:
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Single |
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Keywords:
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Data Mining, Prediction, CPT, Chronic disease, Cardiovascular, Heart Failure |
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Type:
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Full Paper |
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First Page:
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109 |
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Last Page:
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116 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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Cardiovascular diseases are the leading causes of death worldwide, from which heart attack and coronary artery disease are the most common ones. Both diseases are direct causes of Heart Failure, which affects the hearts pumping job, leading to an abnormal distribution of nutriments and oxygen to the body. Data mining techniques have been used to predict different heart diseases. This paper describes the application of a data mining technique, namely the sequence prediction algorithm, CPT+ or Compact Prediction Tree plus, to predict the absence and the presence of heart failure in patients. To evaluate the efficiency of adopted technique, we ran the algorithm in a dataset from the UCI Machine Learning Repository, containing a list of 14 attributes, including the attribute to be predicted. The evaluation showed promising results in term of predicting the absence and presence of heart disease leading to heart failure. |
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