"NB-SVM Hybrid Model for Predicting Cardiovascular Disease"

What is it about?

One of the leading causes of death is heart disease. The prediction of cardiovascular disease remains as a significant challenge in the clinical data analysis domain. Although predicting cardiac disease with a high degree of accuracy is highly challenging, it is possible with Machine Learning (ML) approaches. The implementation of an effective ML system can minimize the need for additional medical testing, minimize human intervention, and predict cardiovascular diseases with high accuracy. This type of assessment can reduce the disease’s severity and mortality rate. Only a few studies show how machine learning techniques might forecast cardiac disease. This study presents a method for improving cardiovascular disease prediction accuracy using Machine Learning (ML) technologies. Various feature combinations and many known classification techniques are used to develop various cardio vascular disease prediction models. The proposed hybrid Machine Learning (ML) prediction model for heart disease leverages a higher degree of performance and accuracy.

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Why is it important?

Heart disease is a leading cause of death worldwide, making the development of effective predictive tools a priority for healthcare systems. Utilizing ML to predict cardiovascular disease addresses this significant health challenge directly. The use of ML technologies, especially hybrid models like NB-SVM, can enhance the accuracy of cardiovascular disease predictions. This accuracy is crucial for early detection and intervention, which can significantly affect patient outcomes

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