Evaluating the Effectiveness of Heart Disease Prediction
Keywords:
Heart Disease Prediction, ROC curves, Machine Learning, Dataset, PerformanceAbstract
Heart disease is one of the major causes of death worldwide, it is crucial to discover problems with your health as soon as possible. To assess the efficacy of heart disease prediction models in accurately identifying individuals at risk, a performance analysis of these algorithms was conducted. A comprehensive dataset was gathered, encompassing patients both with and without cardiac disease, and incorporating diverse clinical and demographic variables. A number of machines learning methods, including logistic regression, decision trees, random forests, support vector machines, and artificial neural networks, were used to develop predictive models. Additionally, receiver operating characteristic (ROC) curves were employed to look into how well specificity and sensitivity work together. The analysis's findings showed that all examined models performed well in predicting heart disease. However, certain models exhibited superior performance in specific metrics. This information is crucial for healthcare professionals, as it enables informed decision-making regarding the selection of prediction models based on the desired balance between correctly identifying positive cases and minimizing false positives. The insights gained from this performance analysis offer valuable guidance on the strengths and limitations of different heart disease prediction models. They can inform future research endeavors and assist healthcare practitioners in implementing effective and accurate prediction systems that identify individuals at risk and facilitate timely interventions.
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