Abstract
Abstract: Heart disease is a major health concern globally, and it is a leading cause of death, especially in developing countries in Africa and Asia. Early detection of heart disease can play a crucial role in preventing its occurrence and reducing its impact. This is where the use of deep neural networks (CNNs) and the Django framework can be highly beneficial. The CNN algorithm is a machine learning technique that uses multiple layers of artificial neural networks to analyze complex data and identify patterns. In the case of heart disease prediction, the CNN algorithm can be trained on a dataset such as the UCI dataset, which contains a large amount of data related to heart disease risk factors and patient characteristics. The dataset can be split into training and testing data to enable the algorithm to learn and validate its accuracy.
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