• Fetenech Meskele Fetenech Meskele
• Dr.Mohd Abdul Hameed
• Dr.O.Jamsheela

The interpretation of problem is a significant and tedious task in medicine. 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 utilise knowledge and experience of numerous specialists and clinical screening data of patients collected earlier to facilitate the interpretation process is considered a valuable option. This paper is presented as an efficient approach for the prediction of heart stroke risk levels from the heart patients’ dataset (Cleveland dataset) by using Machine Learning techniques. The earlier researchers have used k-means mafia algorithm and the accuracy of the algorithm was 74%. Implementing the same algorithm with a modification of k-means with Apriori and ID3 algorithm achieved 85% of the accuracy, which is a growth of 15% in accuracy .

"K-MEANS with APRIORI & ID3 for HEART STROKE RISK LEVEL PREDICTION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 5, page no.17-24, May-2019, Available :http://www.jetir.org/papers/JETIR1905104.pdf

"K-MEANS with APRIORI & ID3 for HEART STROKE RISK LEVEL PREDICTION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 5, page no. pp17-24, May-2019, Available at : http://www.jetir.org/papers/JETIR1905104.pdf