Why the study?
Data classification is computationally difficult, and there is a need to overview and compare available machine learning and soft computing-based techniques for predicting cardiac disease.
This review summarizes various machine learning and soft computing techniques used for the prediction and classification of heart disease.
Provides overview of data classification for illness prediction; leaves open rigorous testing in cardiovascular populations.
Data mining techniques have been used by several researchers to detect illnesses. Some methods are intended to predict a single sickness, while others are intended to predict a wide variety of diseases. It is also possible to improve the accuracy of sickness prediction. In this post, we provided an overview of data classification approaches that are available. These algorithms are mostly represented by themselves. The classification of data is a common and computationally difficult procedure. We've also established the foundation for data categorization. We would compare the best algorithms from a huge set of existing algorithms. This article presents a summary of the research on machine learning and soft computing-based methods for categorizing and predicting cardiac disease.
No takes yet. Share an insight, caveat, or question.
Guna Sekhar Sajja (2021) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: