Analysis of Heart Disease Using Parallel and Sequential Ensemble Methods With Feature Selection Techniques

Abstract

This paper has organized a heart disease-related dataset from UCI repository. The organized dataset describes variables correlations with class-level target variables. This experiment has analyzed the variables by different machine learning algorithms. The authors have considered prediction-based previous work and finds some machine learning algorithms did not properly work or do not cover 100% classification accuracy with overfitting, underfitting, noisy data, residual errors on base level decision tree. This research has used Pearson correlation and chi-square features selection-based algorithms for heart disease attributes correlation strength. The main objective of this research to achieved highest classification accuracy with fewer errors. So, the authors have used parallel and sequential ensemble methods to reduce above drawback in prediction. The parallel and serial ensemble methods were organized by J48 algorithm, reduced error pruning, and decision stump algorithm decision tree-based algorithms. This paper has used random forest ensemble method for parallel randomly selection in prediction and various sequential ensemble methods such as AdaBoost, Gradient Boosting, and XGBoost Meta classifiers. In this paper, the experiment divides into two parts: The first part deals with J48, reduced error pruning and decision stump and generated a random forest ensemble method. This parallel ensemble method calculated high classification accuracy 100% with low error. The second part of the experiment deals with J48, reduced error pruning, and decision stump with three sequential ensemble methods, namely AdaBoostM1, XG Boost, and Gradient Boosting. The XG Boost ensemble method calculated better results or high classification accuracy and low error compare to AdaBoostM1 and Gradient Boosting ensemble methods. The XG Boost ensemble method calculated 98.05% classification accuracy, but random forest ensemble method calculated high classification accuracy 100% with low error.

Detection of Bovine Species on Image Using Machine Learning Classifiers

“…It eliminates the overfitting problem by making regularization [23]. Because of the ability to work in parallel while trees are being formed, training is performed faster than other boosting algorithms [21,24]. Many XGBoosts are used in the literature, and in most studies, higher results are obtained than other algorithms [25 -27].…”

A Powerful Paradigm for Cardiovascular Risk Stratification Using Multiclass, Multi-Label, and Ensemble-Based Machine Learning Paradigms: A Narrative Review

“…Since the number of gold standards are important during the multi-label paradigm, the pie-chart shows the statistical distribution of the different studies using the number of gold standards. The number of studies (given in curly braces) that used the following feature selection techniques were 2D convolutional neural network (CNN) (6) [71,79,81,89,101,111], continuous wavelet transform (1) [72], principal component analysis (PCA) (9) [76,79,84,98,102,112,114,119,121], Mel frequency cepstral coefficient (1) [77], amplitude magnitude (1) [78], gain ratio (1) [80], Matlab (1) [86], association technique (2) [87], SHAP (1) [90], extreme gradient boost (XG-Boost) (1), genetic algorithm (5) [91,103,104,122,123], Tunicate Swarm (1) [116], chi-Square (2) [117], least absolute shrinkage and selection operation (LASSO) (1) [99] (Figure 2e). The increasing trend of CVD publications from the year 2009 to 2021 is shown in Figure 2f.…”

“…The ML-based systems also lead to bias as it lacks clinical evaluation which is discussed in the next section. OBBM, LBBM CVD ML Jan et al [101] OBBM, LBBM, ECG HD ML Jamthikar et al [102] OBBM, LBBM, CUSIP CAD, ACS ML Jothiprakash et al [103] OBBM, LBBM CVD ML Liu et al [104] OBBM, LBBM CA ML Miao et al [105] OBBM, LBBM, ECG CHD ML Mienye et al [106] OBBM, LBBM HD ML Negassa et al [107] OBBM, LBBM HF ML Nakanishi et al [80] OBBM, LBBM, CT Death ML Plawiak et al [108] OBBM, LBBM, ECG Arrhythmia DL Puvar et al [180] OBBM, LBBM, ECG HD ML Reddy et al [109] OBBM, LBBM HD ML Rousset et al [110] OBBM, LBBM CVD ML Sherly et al [111] OBBM, LBBM, ECG HD ML Sherazi et al [112] OBBM, LBBM CVE ML Tan et al [113] OBBM, LBBM CVD ML [114] OBBM, LBBM CVD ML Velusamy et al [115] OBBM, LBBM CAD ML Wankhede et al [116] OBBM, LBBM HD DL Yadav et al [117] OBBM, LBBM HD ML Ye et al [118] OBBM, LBBM HYT ML Yekkala et al [119] OBBM, LBBM CVD ML Zarkogianni et al [120] OBBM, LBBM CVD, Dia ML Zhenya et al […”

An Intelligent Decision Support Ensemble Voting Model for Coronary Artery Disease Prediction in Smart Healthcare Monitoring Environments

“…They have utilized various machine learning prediction approaches and achieved remarkable performance. This section provides an extensive literature review of research studies in the field of heart disease diagnosis supported by machine learning techniques: In [9] Yadav et al presented a novel method for ensemble machine learning utilizing Pearson correlation and chi-square feature selection-based algorithms for the correlation strength of heart disease attributes and the Random Forest ensemble method for the diagnosis of heart disease. The authors performed experiments with their proposed system on the CHDD dataset, and they were able to achieve the best performance considering many evaluation metrics Correctly Classified Instances, Mean absolute error, Incorrectly Classified Instances, Kappa statistic, Root relative squared error, Relative absolute error, and root mean squared error, the Random Forest ensemble method outperforms various machine learning techniques RF, AdaBoostM1, Gradient Boosting.In [10] Li et al proposed a high-performance and intelligent approach for detecting cardiac diseases, and the model is based on a feature selection method (FCMIM) with a support vector machine classifier (SVM).…”