Heart Diseases Prediction Using Machine Learning

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Abstract
Heart disease is a major global health problem, and early is critical to improve patient outcomes. In this study, we look at how machine learning algorithms can be used to predict cardiac disease. To classify the presence or absence of cardiac disease, we use a dataset containing several patient variables and many famous machine learningalgorithms. Logistic Regression, K-Nearest Neighbours, Support Vector Machines, Naive Bayes, Decision Tree Classifier, Gradient Boosting Classifier, and MLP Classifier are among the models investigated. The characteristics needed to train and evaluate the models are extracted after preprocessing the dataset. To examine the prediction capabilities, we use accuracy scores and confusion matrices to evaluate the performance of each model. We hope to find the most effective machine learning model for predicting cardiac disease through experimentation and analysis. Our findings show that machine learning algorithms have the capacity to effectively forecast cardiac disease. The results show that [name the best-performing model] accurately classifies patients with heart disease. The model’s effectiveness is bolstered further by the accompanying confusion matrix, which provides insights into classification errors and identifies opportunities for development. The successful implementation of machine learning models for heart disease prediction could help to develop efficient diagnostic tools and help healthcare practitioners make educated decisions. The incorporation of these models into clinical practise has the potential to improve patient care and lower the burden of heart disease on individuals and healthcare systems.

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