Abstract
Heart disease is a major health concern which is responsible for cause of death worldwide. The different factors that cause heart disease includes inadequate blood flow due to fatty plaques in the arteries, stress, family history etc. Early and accurate identification of heart disease can help people take appropriate preventative action, by which the death rate can be reduced. Artificial neural networks (ANNs), have shown good results in different medical applications as they are able to learn complex patterns from the large datasets. An ANN-based models have the capacity to learn intricate patterns from tremendous datasets, thus it is possible to early detection and medication. In this paper, we introduce a machine learning method that makes use of ANNs to predict heart disease utilizing a variety of risk indicators, including age, sex, blood pressure, cholesterol levels, and other important clinical features. The model is trained on this dataset.
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