Using Anti-Malondialdehyde Modified Peptide Autoantibodies to Import Machine Learning for Predicting Coronary Artery Stenosis in Taiwanese Patients with Coronary Artery Disease

Hsu, Yu-Cheng; Tsai, I-Jung; Hsu, Hui‐Ching; Hsu, Po-Wen; Cheng, Ming-Hui; Huang, Yingli; Chen, Jinhua; Lei, Meng‐Huan; Lin, Ching Yu

doi:10.3390/diagnostics11060961

Cited by 5 publications

(3 citation statements)

References 49 publications

(60 reference statements)

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“…In our previous study, we had reported that the autoantibodies against four different MDA modified peptides may serve as biomarkers in diagnosing patients with CAD [ 23 ]. The MDA-modified positions were underlined: 76 -ADYEK HK VYACEVTHQGLSSPVTK- 99 (IGKC 76–99 ), 284 -LQHLENELT H DIITK- 298 (alpha-1-antitrypsin, A1AT 284–298 ), 824 -VSVQLEASPAFLAVPVE K - 841 (alpha-2-macroglobulin, A2M 824–841 ,), and 4022 -WNFYYSPQSSPD KK LTIF K - 4040 (apolipoprotein B-100, ApoB100 4022–4040 ).…”

Section: Resultsmentioning

confidence: 99%

Autoantibodies to Oxidatively Modified Peptide: Potential Clinical Application in Coronary Artery Disease

Tsai

Shen

et al. 2022

Diagnostics

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Coronary artery disease (CAD) is a global health issue. Lipid peroxidation produces various by-products that associate with CAD, such as 4-hydroxynonenal (HNE) and malondialdehyde (MDA). The autoantibodies against HNE and MDA-modified peptides may be useful in the diagnosis of CAD. This study included 41 healthy controls (HCs) and 159 CAD patients with stenosis rates of <30%, 30–70%, and >70%. The plasma level of autoantibodies against four different unmodified and HNE-modified peptides were measured in this study, including CFAH1211–1230, HPT78–108, IGKC2–19, and THRB328–345. Furthermore, feature ranking, feature selection, and machine learning models have been utilized to exploit the diagnostic performance. Also, we combined autoantibodies against MDA and HNE-modified peptides to improve the models’ performance. The eXtreme Gradient Boosting (XGBoost) model received a sensitivity of 78.6% and a specificity of 90.4%. Our study demonstrated the combination of autoantibodies against oxidative modification may improve the model performance.

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Section: Resultsmentioning

confidence: 99%

Autoantibodies to Oxidatively Modified Peptide: Potential Clinical Application in Coronary Artery Disease

Tsai

Shen

et al. 2022

Diagnostics

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“…In addition, different classifiers have been chosen to predict CAD according to their performance. Reports from previous studies done by Hsu et al (2021) show that random forest can be used as an effective algorithm to predict CAD based on the area under the receiver operating characteristic (ROC) curve (AUC) around 0.94. Muhammad et al (2021) used machine learning predictive models for CAD prediction, and among the used models, random forest stands as the best model with an AUC of 0.92.…”

Section: Related Workmentioning

confidence: 99%

Predicting Coronary Artery Disease Using Machine Learning

Raj Dahal,

Raj Pokhrel,

Subedi

et al. 2024

IJSP

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Developing a predictive model for detecting Coronary Artery Disease (CAD) is crucial due to its high global fatality rate of approximately 17.9 million people annually. With the advancements in artificial intelligence, the availability of large-scale data, and increased access to computational capability, it is feasible to create robust models that can detect CAD with high precision. This study aims to build a predictive model that can assist health workers in the timely detection of CAD and ultimately reduce mortality. This study performs a comparative analysis of four supervised classification machine learning algorithms- Logistic regression (LR), Support vector machine (SVM), Extreme gradient boosting (XGBoost), and Artificial neural network (ANN),  in predicting the case-control status of the patient. Chi-squared and lasso criteria are employed to select the most relevant ones from the available features. The performance of the employed models is compared using sensitivity, specificity, accuracy, and the area under the receiver operating characteristic (ROC) curve (AUC). The experimental results indicate that the LR model is the most effective and accurate among the models tested, and its implementation can improve the detection of CAD in clinical settings.

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“…Authors have performed the machine learning models for coronary artery disease in Nigeria and the RF model emerged as the best model for performing the predictions, in terms of accuracy followed by the best sensitivity results for the support-vector machine learning model [30]. The RF classifier achieved remarkable results for the AUC value for predicting the coronary artery stenosis in Taiwanese patients with coronary artery disease [31].…”

Section: Literature Reviewmentioning

confidence: 99%

XAI Framework for Cardiovascular Disease Prediction Using Classification Techniques

Guleria¹,

Srinivasu

Ahmed

et al. 2022

Electronics

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Machine intelligence models are robust in classifying the datasets for data analytics and for predicting the insights that would assist in making clinical decisions. The models would assist in the disease prognosis and preliminary disease investigation, which is crucial for effective treatment. There is a massive demand for the interpretability and explainability of decision models in the present day. The models’ trustworthiness can be attained through deploying the ensemble classification models in the eXplainable Artificial Intelligence (XAI) framework. In the current study, the role of ensemble classifiers over the XAI framework for predicting heart disease from the cardiovascular datasets is carried out. There are 303 instances and 14 attributes in the cardiovascular dataset taken for the proposed work. The attribute characteristics in the dataset are categorical, integer, and real type and the associated task related to the dataset is classification. The classification techniques, such as the support vector machine (SVM), AdaBoost, K-nearest neighbor (KNN), bagging, logistic regression (LR), and naive Bayes, are considered for classification purposes. The experimental outcome of each of those algorithms is compared to each other and with the conventional way of implementing the classification models. The efficiency of the XAI-based classification models is reasonably fair, compared to the other state-of-the-art models, which are assessed using the various evaluation metrics, such as area under curve (AUC), receiver operating characteristic (ROC), sensitivity, specificity, and the F1-score. The performances of the XAI-driven SVM, LR, and naive Bayes are robust, with an accuracy of 89%, which is assumed to be reasonably fair, compared to the existing models.

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Using Anti-Malondialdehyde Modified Peptide Autoantibodies to Import Machine Learning for Predicting Coronary Artery Stenosis in Taiwanese Patients with Coronary Artery Disease

Cited by 5 publications

References 49 publications

Autoantibodies to Oxidatively Modified Peptide: Potential Clinical Application in Coronary Artery Disease

Autoantibodies to Oxidatively Modified Peptide: Potential Clinical Application in Coronary Artery Disease

Predicting Coronary Artery Disease Using Machine Learning

XAI Framework for Cardiovascular Disease Prediction Using Classification Techniques

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