Heart disease is one of the most widely spreading and deadliest diseases across the world.
In this study, we have proposed hybrid model for heart disease prediction by employing …

Feature selection improves the classification performance of machine learning models. It
also identifies the important features and eliminates those with little significance …

Heart disease is one of the causes for death throughout the world. Heart disease cannot be
easily identified by the medical experts and practitioners as the detection of heart disease …

Disease classification problem can be solved by building machine learning models by
training a machine to identify disease classes. The classification of disease is achieved by …

In this study, the author proposed k-nearest neighbor (KNN) based heart disease prediction
model. The author conducted an experiment to evaluate the performance of the proposed …

Globally, heart disease is a major health concern, and precise forecasting of its occurrence
is essential for early diagnosis along with prevention. The lack of extensive comparison and …

Data mining refers to analyzing an existing dataset related to heart patients from which
patterns are discovered within the dataset, and hence we are able to find out meaningful …

The goal of this study is to determine the early and accurate prediction of heart disease
using machine-learning techniques and feature selection methods. The study develops a …

In the field of medical regularly handles enormous amounts of data. Handling huge data by
conventional methods can affect the results. Algorithms for machine learning can be used to …