Cardiovascular Disease Detection using Ensemble Learning

Alqahtani, Abdullah; Alsubai, Shtwai; Sha, Mohemmed; Vilčeková, Lucia; Javed, Talha

doi:10.1155/2022/5267498

Cited by 49 publications

(15 citation statements)

References 25 publications

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“…The authors suggested that hybrid approach can be used to extend the existing work for better prediction. Alqahtani et al [17] proposed a framework for cardiovascular disease prediction using ensemble learning and deep learning techniques. In the experiment, the random forest algorithm achieved the highest accuracy (88.65%), precision (90.03), recall (88.03), f1-score (88.02), and ROC-AUC value (92).…”

Section: Related Workmentioning

confidence: 99%

“…Techniques such as data imputation for handling missing values, detection, and replacement of outliers using the Boxplot method have been used to achieve better results than other related works. Boosting, bagging, stacking, and majority vote Cleveland heart disease dataset (publicly available) 85.48% with majority vote [10] Recursive feature elimination and GB Do 89.78% [12] XGB with Bayesian optimization Do 91.80% [14] CatBoost, GB, XGB, and ADB Do 83.60% with ADB [17] DNN, KDNN, XGB, KNN, decision tree, and random forest Do 88.65% with random forest [18] Naïve Bayes, linear model, logistic regression, decision tree, random forest, SVM, and HRFLM Do 88.40% with HRFLM Our method XGB, ADB, and GB Do 92.20% for BDT…”

Section: Comparative Analysismentioning

confidence: 99%

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An Improved Ensemble Learning Approach for Heart Disease Prediction Using Boosting Algorithms

Ganie¹,

Pramanik²,

Malik³

et al. 2023

Computer Systems Science and Engineering

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Cardiovascular disease is among the top five fatal diseases that affect lives worldwide. Therefore, its early prediction and detection are crucial, allowing one to take proper and necessary measures at earlier stages. Machine learning (ML) techniques are used to assist healthcare providers in better diagnosing heart disease. This study employed three boosting algorithms, namely, gradient boost, XGBoost, and AdaBoost, to predict heart disease. The dataset contained heart disease-related clinical features and was sourced from the publicly available UCI ML repository. Exploratory data analysis is performed to find the characteristics of data samples about descriptive and inferential statistics. Specifically, it was carried out to identify and replace outliers using the interquartile range and detect and replace the missing values using the imputation method. Results were recorded before and after the data preprocessing techniques were applied. Out of all the algorithms, gradient boosting achieved the highest accuracy rate of 92.20% for the proposed model. The proposed model yielded better results with gradient boosting in terms of precision, recall, and f1-score. It attained better prediction performance than the existing works and can be used for other diseases that share common features using transfer learning.

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Section: Related Workmentioning

confidence: 99%

Section: Comparative Analysismentioning

confidence: 99%

An Improved Ensemble Learning Approach for Heart Disease Prediction Using Boosting Algorithms

Ganie¹,

Pramanik²,

Malik³

et al. 2023

Computer Systems Science and Engineering

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“…The GWO approach is used to select features, and an SVM model is then used to predict heart disease using the resulting subset of features [2]. Singh and Kumar proposed various machine learning models that can be used for heart disease prediction [3] whereas Javed et al [4] proposed a different approach by using ensemble learning for heart disease prediction. Eid Emaryet al [5] suggested a system to follow when using GWO algorithm integrated in machine learning.…”

Section: Literature Surveymentioning

confidence: 99%

A Modified Grey Wolf Optimizer algorithm for feature selection to predict heart diseases

2023

ijfans

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Globally, heart disease is a leading cause of illness and mortality. This impacts people from all around the world. Accurate prediction of the risk of heart disease is crucial for early detection and prevention.For this, large amounts of features/attributes need to be stored and analyzed to diagnose a patient. Storing many features can lead to substandard management of data. We need to store only the chief features. In this study, we proposed a modified grey wolf optimizer for feature selection.The resultant subset of features is then used to predict the risk of having a heart disease using machine learning model, Support Vector Machine (SVM). We compared the proposed algorithm with the existing GWO-SVM algorithm. We evaluated the effectiveness of the proposed algorithm using accuracy, sensitivity, and specificity metrics. Our results show that, using the modified grey wolf algorithm for feature selection and using SVM weobtained an accuracy of 95.82%, specificity of 94.64%, and sensitivity of 96.86%. The results show the proposed algorithm's capability for predicting the risk of heart disease and could contribute to the development of more accurate and efficient predictive models for heart disease risk assessment.

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“…In [1] proposed an ensemble model on a dataset consisting of 70000 records with 13 attributes. Random Forest is used for feature selection and Pearson's Coefficient to examine correlation between features.…”

Section: Literature Reviewmentioning

confidence: 99%

Heart Disease Detection Using Machine Learning and Deep Learning

2023

ijfans

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Heart is responsible for different functions like blood circulation and supplying oxygen.These days, heart disease has become one of the major causes of death of many people. It is caused by different reasons like having an unhealthy lifestyle and having high levels of blood pressure, cholesterol, and other conditions. When the patient is detected with the presence of heart disease, he could be monitored and treated to save their lives. An ensemble model is built to detect the presence of disease by using various machine learning and deep learning models. Initially, the unwanted features are removed from the data by using feature selection methods like correlation matrix and fisher score. The ML models are then trained with the data and are stacked with a meta model. The stacking model is ensembled with the deep learning models used. K-Fold cross validation technique is used to train the models. The built ensemble model gave higher accuracy of 87.2%.

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Cardiovascular Disease Detection using Ensemble Learning

Cited by 49 publications

References 25 publications

An Improved Ensemble Learning Approach for Heart Disease Prediction Using Boosting Algorithms

An Improved Ensemble Learning Approach for Heart Disease Prediction Using Boosting Algorithms

A Modified Grey Wolf Optimizer algorithm for feature selection to predict heart diseases

Heart Disease Detection Using Machine Learning and Deep Learning

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