An Ensemble Feature Optimization for an Effective Heart Disease Prediction Model

Abstract

The use of machine learning (ML) within medical field is on the rise, notably as a means to enhance both the speed and precision of diagnosis. Through evaluating large volumes of patient information, machine learning is able to provide disease prediction, giving both patients and doctors more control over their health. Predicting and preventing heart disease has become a major area of study in medical data processing as a result of the increased expense of therapy. Since there are so many factors that come into play, estimating one's heart disease risk manually is a challenging task. Moreover, there are very few methods which provide better accuracy for the prediction of the heart disease. Hence, by using openly accessible cleveland heart disease dataset, this research aims to design and evaluate several advanced technologies constructed employing machine leaning algorithms for diagnosing if an individual is going to get heart disease or not. In this paper, we propose an ensemble feature optimized (EFO) learning method which uses an enhanced extreme gradient boosting tree and feature level cross validation scheme for effective heart disease (EHD) prediction. The presented EFO prediction algorithm and other existing machine learning algorithms have been used for the prediction of the heart diseases. The performance of the existing algorithms (XGB-based, ensemble tree hyper optimization (ETHO), and MLP-PSO) and proposed EFO algorithm has been evaluated using the classification metrics. When compared with the XGB-based, ensemble tree hyper optimization (ETHO), and MLP-PSO algorithm, the EFO algorithm has attained an accuracy of 98.61%. The EFO algorithm provides the doctors to able to predict the heart disease more efficiently and effectively.

Heart Disease Prediction Using Random Forest Based Hybrid Optimization Algorithms

“…The optimized XGBoost with OH encoded [21] model consumes a significant amount of data while working with large datasets. The EFO [24] model has high error rate for multiclass classification. The proposed BAPSO-RF approach overcomes these existing model limitations.…”

“…This section illustrates the comparative analysis of proposed BAPSO-RF approach with evaluation metrics like accuracy, precision, recall and f1-score as shown in Table 7. The existing result such as [17], [18,20,21,24] are utilized for estimating an ability of the classifier. The BAPSO-RF is trained, tested and validated by using UCI heart disease dataset.…”

Class imbalance aware drift identification model for detecting diverse attack in streaming environment

“…The XGBoost algorithm has been introduced in this section. The XGBoost model is a means of improving upon a decision-tree (DT) model by combining boosting with a gradient-descent method [23].…”

A secure framework for effective workload resource management

“…The extreme gradient boosting (XGBoost) method is an algorithm that is used for regression and classification regression tasks, and it can be used for identifying malicious packets at a gateway server in an IoT environment. Further, the XGBoost algorithm is a type of gradient boosting algorithm that builds a series of decision trees, where each new tree is built to correct the errors of the previous tree [26]. This approach allows XGBoost to achieve high accuracy in classification tasks, making it suitable for identifying malicious packets in an IoT environment.…”