Abstract
Abstract: Heart disease is a common, potentially fatal disorder that affects a lot of people all over the world. A thorough and timely diagnosis is essential for efficient treatment and management. By analyzing large datasets, deep learning algorithms have demonstrated considerable promise in the diagnosis of cardiac disease. Given the alarming prevalence of heart diseases and their substantial impact on mortality rates, researchers worldwide have dedicated substantial efforts to address this issue. They have approached heart diagnosis as a classification problem, employing data mining techniques to identify meaningful patterns. This project specifically focuses on supervised learning, employing various algorithms such as regression (including linear regression, support vector machine, and Poisson regression) and classification (including logistic regression, decision tree, random forest, and naive Bayes)
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