• Shivani Aggarwal
• Arvind Kumar
• Sonam Kashyap

– Heart disease is one of the major health problem in this modern era. In a survey by Public Health Foundation Of India in 2016 about 5,681 per 1,00,000 population in India suffered from heart disease. This paper gives a brief description about prediction of heart disease using data mining algorithms. The major reason to use data mining technologies is increasing amount of data in healthcare sector which is “ not minded”. Data mining is the exploration of hidden and previously unknown pattern, relationship and knowledge which is hard to detect using traditional tools. Data mining techniques help researchers to develop an intelligent heart disease prediction system. In this paper, we use the data mining methods for the risk prediction of heart disease like Naïve Bayes, Decision Tree, Random Forest and Logistic Regression. This paper also gives a comparative study of these algorithms using a web application Jupyter Notebook. The performance of these algorithms are compared according to their accuracies.

Heart disease, Data mining, Naïve Bayes, Decision Tree, Random Forest, Logistic Regression.

"Prediction On Heart Disease Using Data Mining Algorithms", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 2, page no.483-489, February-2019, Available :http://www.jetir.org/papers/JETIR1902B70.pdf

"Prediction On Heart Disease Using Data Mining Algorithms", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 2, page no. pp483-489, February-2019, Available at : http://www.jetir.org/papers/JETIR1902B70.pdf