Fuzzy C-Means with APRIORI & ID3 for Predicting Heart Stroke Risk Level
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Abstract
The past decades have brought many remarkable researches in diagnosis of
disease. The interpretation of the problems in medicine is a significant and tedious
task. The detection of heart problem from various factors or symptoms is an issue which is not free from false presumptions often accompanied by unpredictable effects. Thus the effort to utilize knowledge and experience of numerous specialists and clinical data of patients collected earlier to facilitate the interpretation process is considered as a
valuable asset. This paper introduces an efficient approach to predict heart stroke risk levels from the heart problem dataset by using machine learning technique. Earlier researchers have used k-means based mafia algorithm and the accuracy was 74%. When modifying the algorithm with fuzzy c-means, the accuracy is increased to 89%. There is a 15% improvement while comparing to the earlier algorithm.
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