Development and analysis of a comprehensive diagnostic model for aortic valve calcification using machine learning methods and artificial neural networks

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SUMMARY

    Accordingly, the purpose of this work was to construct and test a machine_learning-based diagnostic model for AVC patients, as well as to get thorough knowledge of the immunological processes underlying AVC development and to screen potential small_molecule medicines. Support vector machine (SVM) is a classification and regression technique that is often used in supervised machine_learning, and the RFE technology is used to choose the best gene from the metadata queue to reduce overfitting. Development and validation of diagnostic models Aortic valve calcification diagnosis using machine_learning Five markers were obtained by combining two machine_learning . . .

     

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