Heart Disease Prediction Using Machine Learning
Golden Nancy
R. Jairus Happylin Pandian
V. James Starvin
R. Godwin Kiruba
H. Abisheak
Keywords: Heart Disease, Machine Learning, SVM.
Abstract
Heart disease is one of the most common diseases. This disease is now very common, we use various characteristics that can be well correlated with this heart disease to find a better prediction method and also use SVM algorithm for prediction. Cardiovascular disease, also known as cardiovascular disease, encompasses a variety of diseases affecting the heart and has been the leading cause of death worldwide over the past few decades.
Data mining is a common technique for processing large amounts of data in the healthcare industry. This machine learning model can help estimate the probability of death due to heart failure by extracting important features from a dataset and making predictions based on those features. Machine learning is widely used today in many business applications such as e-commerce. Prediction is one of the domains where this machine learning is applied, and our topic is predicting myocardial infarction by working with patient datasets and patient data to predict the likelihood of myocardial infarction.
All experiments are performed in a simulated environment and carried out on the PYTHON FLASK platform. The proposed work can be used to predict the outcomes of machine learning techniques. Conduct research to predict accuracy. Future studies may predict other different parameters, and studies may be classified according to other parameters.
