Fuzzy C-Means with APRIORI & ID3 for Predicting Heart Stroke Risk Level

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Jamsheela O
Mohd Abdul Hameed
Fetenech Meskele


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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How to Cite
O, J., Hameed, M. A., & Meskele, F. (2019). Fuzzy C-Means with APRIORI & ID3 for Predicting Heart Stroke Risk Level. INFOCOMP Journal of Computer Science, 18(2), 01-07. Retrieved from http://infocomp.dcc.ufla.br/index.php/infocomp/article/view/611