• Ruchi Panwar
• Amit Kishor
• Pankaj pratap singh
• Anirudh Kumar tripathi

ABSTRACT: Myocardial infarction has become the main root of death in the world. In the field of medical science myocardial infarction detection is one of the growing area. Due to Heart disease one person kills in every 50 seconds-“According to American heart association’’ some of these death are occur due to the impaired blood supply and also even before the patient reach the hospital. Massive amount of patient’s related data is also maintained day by day. The stored data can also helped in detecting the chance of future disease and extraction of knowledge can be solved by the data mining technique. The main aim of this comparative study would be enable patients to become better informed about their condition and also motivate them to seek better care earlier in any situation for the detection of myocardial infarction. The data was collected from kaggle UCI repository, 10 attributes of clinical factors can be reported by the patients were studied. Researchers have already applied the multi linear regression, neural network and logistic regression individually but in our research work multi linear regression, neural network and logistic regression with accurate detection to detect the risk of myocardial infarction multi linear regression accuracy is 65.9% neural network accuracy is 77.9% and the best logistic regression model in the terms of performance accuracy 81.9%.

"Predicting the risk of Myocardial Infarction using Multi linear Regression, Neural Network and Logistic Regression: A Comparative study ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.106-115, June 2019, Available :http://www.jetir.org/papers/JETIR1906I45.pdf

"Predicting the risk of Myocardial Infarction using Multi linear Regression, Neural Network and Logistic Regression: A Comparative study ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp106-115, June 2019, Available at : http://www.jetir.org/papers/JETIR1906I45.pdf