• Aman Jain
• Simran Sharma

Health Care Clinical diagnosis usually ends with the knowledge and practice of the physician. The computer-assisted decision support system plays a major role in the medical field. Provides the extraction of methodological data and technology to change these hills of data into useful information for decision-making. Using data extraction techniques, it takes less time to predict the disease more accurately. Researchers use many data mining techniques to help health professionals diagnose heart disease. But the use of data mining technology can reduce the number of tests required. In order to reduce the number of deaths due to heart disease, a rapid and effective detection technique is essential. The decision tree is one of the effective methods for extracting the data used. This research compares different algorithms. The algorithms tested are the Decision Tree algorithm, the Naive Bays algorithm, the support vector machine and the Random Forest algorithm. Current data sets are being used for heart patients in the Cleveland UC database to test and test the performance of tree resolution algorithms. These datasets include 303 observations and 76 attributes. Later, a classification algorithm offering the ideal potential for use in large data will be proposed. The purpose of this study is to extract hidden patterns by applying significant data extraction techniques for heart disease and predicting the existence of cardiopathy in patients for whom this presence was assessed by the absence of probable presence.

Data mining technique, machine learning Algorithm, Decision Support System, Health care, Heart Disease

"Heart Disease Predicting Using Machine Learning Algorithms and Data Mining Technique", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 5, page no.120-124, May-2019, Available :http://www.jetir.org/papers/JETIR1905D16.pdf

"Heart Disease Predicting Using Machine Learning Algorithms and Data Mining Technique", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 5, page no. pp120-124, May-2019, Available at : http://www.jetir.org/papers/JETIR1905D16.pdf