Hybrid Feature Selection Algorithm and Ensemble Stacking for Heart Disease Prediction

Intelligent application for Heart disease detection using Hybrid Optimization algorithm

Prediction of heart disease is very important because it is one of the causes of death around the world. Moreover, heart disease prediction in the early stage plays a main role in the treatment and recovery disease and reduces costs of diagnosis disease and side effects it. Machine learning algorithms are able to identify an effective pattern for diagnosis and treatment of the disease and ident...

Ensemble Classification and Extended Feature Selection for Credit Card Fraud Detection

Due to the rise of technology, the possibility of fraud in different areas such as banking has been increased. Credit card fraud is a crucial problem in banking and its danger is over increasing. This paper proposes an advanced data mining method, considering both feature selection and decision cost for accuracy enhancement of credit card fraud detection. After selecting the best and most effec...

Hybrid Correlation and Causal Feature Selection for Ensemble Classifiers

PC and TPDA algorithms are robust and well known prototype algorithms, incorporating constraint-based approaches for causal discovery. However, both algorithms cannot scale up to deal with high dimensional data, that is more than few hundred features. This chapter presents hybrid correlation and causal feature selection for ensemble classifiers to deal with this problem. Redundant features are ...

A Novel Feature Selection Algorithm for Heart Disease Classification

Humans are affected by different life threatening diseases. One among them is heart disease. Medical practitioners pay more attention to this disease. Patients undergo different diagnostic procedures to identify the factors related to the disease. The results of the procedures yield different variations of the disease. However, there may be relevant, redundant and irrelevant features representi...

A Genetic Algorithm to Ensemble Feature Selection