Improvement of the Performance of Models for Predicting Coronary Artery Disease Based on XGBoost Algorithm and Feature Processing Technology

Zhang, Shasha; Yuan, Yunhao; Yao, Zhonghua; Wang, Xinyan; Lei, Zhen

doi:10.3390/electronics11030315

Cited by 8 publications

(4 citation statements)

References 36 publications

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“…The thorough assessment of machine learning models, specifically the XGBoost and K-Nearest Neighbors models, in the context of heart disease prediction, provides valuable insights. These insights align with the research conducted by Zhang et al [41], which underscores the effectiveness of the XGBoost algorithm in this specific domain.…”

Section: Discussionsupporting

confidence: 88%

Machine Learning-Based Predictive Models for Detection of Cardiovascular Diseases

Ogunpola,

Saeed,

Basurra

et al. 2024

Diagnostics

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Cardiovascular diseases present a significant global health challenge that emphasizes the critical need for developing accurate and more effective detection methods. Several studies have contributed valuable insights in this field, but it is still necessary to advance the predictive models and address the gaps in the existing detection approaches. For instance, some of the previous studies have not considered the challenge of imbalanced datasets, which can lead to biased predictions, especially when the datasets include minority classes. This study’s primary focus is the early detection of heart diseases, particularly myocardial infarction, using machine learning techniques. It tackles the challenge of imbalanced datasets by conducting a comprehensive literature review to identify effective strategies. Seven machine learning and deep learning classifiers, including K-Nearest Neighbors, Support Vector Machine, Logistic Regression, Convolutional Neural Network, Gradient Boost, XGBoost, and Random Forest, were deployed to enhance the accuracy of heart disease predictions. The research explores different classifiers and their performance, providing valuable insights for developing robust prediction models for myocardial infarction. The study’s outcomes emphasize the effectiveness of meticulously fine-tuning an XGBoost model for cardiovascular diseases. This optimization yields remarkable results: 98.50% accuracy, 99.14% precision, 98.29% recall, and a 98.71% F1 score. Such optimization significantly enhances the model’s diagnostic accuracy for heart disease.

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Section: Discussionsupporting

confidence: 88%

Machine Learning-Based Predictive Models for Detection of Cardiovascular Diseases

Ogunpola,

Saeed,

Basurra

et al. 2024

Diagnostics

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“…In our previous research, we applied four feature processing techniques and two kinds of class balancing methods to develop a CAD prediction model based on Random Forest algorithm and XGBoost algorithm. The model effectively realized the early detection of CAD and achieved 94.7% prediction accuracy [48].…”

Section: Related Workmentioning

confidence: 96%

Coronary Artery Disease Detection Model Based on Class Balancing Methods and LightGBM Algorithm

et al. 2022

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Coronary artery disease (CAD) is a disease with high mortality and disability. By 2019, there were 197 million CAD patients in the world. Additionally, the number of disability-adjusted life years (DALYs) owing to CAD reached 182 million. It is widely known that the early and accurate diagnosis of CAD is the most efficient method to reduce the damage of CAD. In medical practice, coronary angiography is considered to be the most reliable basis for CAD diagnosis. However, unfortunately, due to the limitation of inspection equipment and expert resources, many low- and middle-income countries do not have the ability to perform coronary angiography. This has led to a large loss of life and medical burden. Therefore, many researchers expect to realize the accurate diagnosis of CAD based on conventional medical examination data with the help of machine learning and data mining technology. The goal of this study is to propose a model for early, accurate and rapid detection of CAD based on common medical test data. This model took the classical logistic regression algorithm, which is the most commonly used in medical model research as the classifier. The advantages of feature selection and feature combination of tree models were used to solve the problem of manual feature engineering in logical regression. At the same time, in order to solve the class imbalance problem in Z-Alizadeh Sani dataset, five different class balancing methods were applied to balance the dataset. In addition, according to the characteristics of the dataset, we also adopted appropriate preprocessing methods. These methods significantly improved the classification performance of logistic regression classifier in terms of accuracy, recall, precision, F1 score, specificity and AUC when used for CAD detection. The best accuracy, recall, F1 score, precision, specificity and AUC were 94.7%, 94.8%, 94.8%, 95.3%, 94.5% and 0.98, respectively. Experiments and results have confirmed that, according to common medical examination data, our proposed model can accurately identify CAD patients in the early stage of CAD. Our proposed model can be used to help clinicians make diagnostic decisions in clinical practice.

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“…Therefore, a confusion matrix is used to obtain performance metrics for the models on the imbalanced datasets. We utilized eight metrics, such as accuracy, precision, recall, F1-score, G-Mean, specificity, AUC-ROC, and Kappa, for evaluating the DNN and CNN models [29,42,[67][68][69][70][71].…”

Section: Evaluation Metricsmentioning

confidence: 99%

Effective Class-Imbalance Learning Based on SMOTE and Convolutional Neural Networks

et al. 2023

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Imbalanced Data (ID) is a problem that deters Machine Learning (ML) models from achieving satisfactory results. ID is the occurrence of a situation where the quantity of the samples belonging to one class outnumbers that of the other by a wide margin, making such models’ learning process biased towards the majority class. In recent years, to address this issue, several solutions have been put forward, which opt for either synthetically generating new data for the minority class or reducing the number of majority classes to balance the data. Hence, in this paper, we investigate the effectiveness of methods based on Deep Neural Networks (DNNs) and Convolutional Neural Networks (CNNs) mixed with a variety of well-known imbalanced data solutions meaning oversampling and undersampling. Then, we propose a CNN-based model in combination with SMOTE to effectively handle imbalanced data. To evaluate our methods, we have used KEEL, breast cancer, and Z-Alizadeh Sani datasets. In order to achieve reliable results, we conducted our experiments 100 times with randomly shuffled data distributions. The classification results demonstrate that the mixed Synthetic Minority Oversampling Technique (SMOTE)-Normalization-CNN outperforms different methodologies achieving 99.08% accuracy on the 24 imbalanced datasets. Therefore, the proposed mixed model can be applied to imbalanced binary classification problems on other real datasets.

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Improvement of the Performance of Models for Predicting Coronary Artery Disease Based on XGBoost Algorithm and Feature Processing Technology

Cited by 8 publications

References 36 publications

Machine Learning-Based Predictive Models for Detection of Cardiovascular Diseases

Machine Learning-Based Predictive Models for Detection of Cardiovascular Diseases

Coronary Artery Disease Detection Model Based on Class Balancing Methods and LightGBM Algorithm

Effective Class-Imbalance Learning Based on SMOTE and Convolutional Neural Networks

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