Detecting Heart Disease Using a Supervised Decision Tree Classifier
Keywords:
supervised, confusion matrix, linear regression, unsupervised, python, reinforcedAbstract
The heart is vital to all living organisms and accurate diagnosis and prognosis of heart disease are crucial, as minor errors can result in severe consequences or loss of life. The incidence of heart-related deaths is increasing rapidly each day. To address this issue, an effective disease prediction system is essential. Machine learning, a branch of artificial intelligence (AI), offers significant support for predicting various events, including those triggered by natural disasters. In this study, we assess the ability of machine learning algorithms to predict heart disease. The algorithms evaluated include SVM (Support Vector Machine), Logistic Regression (LOR), Gaussian Naive Bayes (GNB) and Decision Tree, using UCI benchmark datasets for testing and training. Python, implemented through the Anaconda (Jupyter) notebook, is the preferred tool, offering an assortment of libraries and headers that enhance efficiency and precision.
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