• Bhagyashree S. Shelke
• K. C. Waghmare
• Anil Kumar Gupta
• Amarjeet Sharma

Nowadays, health diseases are increasing rapidly specially the heart disease. So it is necessary to predict the heart disease in early stage to save the number of patients with mortality. Previous studies show that various data mining and machine learning techniques have been used for the prediction of heart disease with different numbers of parameters. Patient’s different parameters are considered essential for the prediction such as blood pressure, heart rate, cholesterol, and so on. In this paper dataset used for experiment purpose is Body Sensor Network (BSN) dataset which contains different attributes. In this paper, the proposed system consists of three algorithms, Q-Learning algorithm, Naive Bayes algorithm and Heart Disease Prediction (HDP) algorithm. Q-Learning and Naive Bayes are two already existing machine learning algorithms and the proposed one is Heart Disease Prediction (HDP) algorithm. Q-Learning and Naive Bayes algorithms provide class label as an output based on the previously defined threshold in the algorithms. The output of these two algorithms used as input to the HDP algorithm. Finally HDP generates patient health report that is prediction result.

Supervised learning, Machine Learning, Heart disease, Data mining techniques, Clustering, and Classification, Prediction system

"HEART DISEASE PREDICTION SYSTEM USING MACHINE LEARNING ALGORITHMS ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.250-254, June-2019, Available :http://www.jetir.org/papers/JETIR1906F42.pdf

"HEART DISEASE PREDICTION SYSTEM USING MACHINE LEARNING ALGORITHMS ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp250-254, June-2019, Available at : http://www.jetir.org/papers/JETIR1906F42.pdf