piRNAs as emerging biomarkers and physiological regulatory molecules in cardiovascular disease

Liu, Zhihua; Zhao, Xi

doi:10.1016/j.bbrc.2024.149906

Cited by 1 publication

(1 citation statement)

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“…Additionally, it is crucial to explore and compare other AutoML frameworks and algorithms, such as H2O AutoML and TPOT, and conduct in-depth comparisons with AutoGluon to determine optimal strategies for specific applications ( 47 , 48 ). The explainability of machine learning models remains a pivotal issue, necessitating more in-depth validation across different medical domains to ensure its consistency with clinical practice ( 49 – 68 ).…”

Section: Discussionmentioning

confidence: 99%

Explainable coronary artery disease prediction model based on AutoGluon from AutoML framework

Wang,

Xue,

Zhang

et al. 2024

Front. Cardiovasc. Med.

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ObjectiveThis study focuses on the innovative application of Automated Machine Learning (AutoML) technology in cardiovascular medicine to construct an explainable Coronary Artery Disease (CAD) prediction model to support the clinical diagnosis of CAD.MethodsThis study utilizes a combined data set of five public data sets related to CAD. An ensemble model is constructed using the AutoML open-source framework AutoGluon to evaluate the feasibility of AutoML in constructing a disease prediction model in cardiovascular medicine. The performance of the ensemble model is compared against individual baseline models. Finally, the disease prediction ensemble model is explained using SHapley Additive exPlanations (SHAP).ResultsThe experimental results show that the AutoGluon-based ensemble model performs better than the individual baseline models in predicting CAD. It achieved an accuracy of 0.9167 and an AUC of 0.9562 in 4-fold cross-bagging. SHAP measures the importance of each feature to the prediction of the model and explains the prediction results of the model.ConclusionThis study demonstrates the feasibility and efficacy of AutoML technology in cardiovascular medicine and highlights its potential in disease prediction. AutoML reduces the barriers to model building and significantly improves prediction accuracy. Additionally, the integration of SHAP enhances model transparency and explainability, which is critical to ensuring model credibility and widespread adoption in cardiovascular medicine.

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Section: Discussionmentioning

confidence: 99%