Discussion
NB
REGRESSION, DISEASE, EVENT
The BRAF Real-time tissue strain elastography helps assess the anatomical structure and biological traits of PTC because it represents the relative hardness of the lesion and its surrounding tissues, whereas the hardness of PTC tissues is closely related to its internal pathological structure. Bojunga et al.In a previous studyMachine learning algorithms benefit from learning from input data and automatically recognizing patterns and trends in that data automatically. There have been numerous studiesIn order to determine whether US-based radiomics could perhaps assess the occurrence of BRAFThe higher AUCs and ACCs in the current study could be attributed to the US elastography radiomic features used, which provided more information than the grayscale US. Also in this study, machine learning algorithms were used in conjunction with radiomics, and several machine learning classifiers were used to build the models. To the best of our knowledge, this is the first study to develop models based on different machine-learning algorithms to predict BRAFComparing the current study results to our previous studyThe AUCs of the KNN (0.87), LDA (0.91), LR (0.92), and NB (0.80) classifiers were all lower than the logistic regression classifier (0.93) utilized in our previous study. The SVM_L AUC (0.93) score, on the other hand, was the same as the logistic regression algorithm (0.93) utilized in our previous work. When compared to the logistic regression classifier (ACC: 0.88 and AUC: 0.93) employed in our previous investigation, the SVM_RBF had higher ACC (0.93) and AUC (0.98) values. The disparity in performance could be attributed to the various feature preprocessing techniques and feature selection strategies used in the current investigation.On US examination, Kabaker et al.The calibration curve of the prediction model is an essential metric for evaluating the probability accuracy of a disease risk model in predicting an individual outcome event in the future. A high degree of calibration shows that the prediction model is accurate, whereas a low degree of calibration indicates that the model may exaggerate or underestimate the risk of illness. The blue line reflects the performance of the machine learning algorithms, while the diagonal dotted line represents an ideal prediction (Fig. Furthermore, the DCA was used to evaluate the clinical utility of these models (Fig. There are some limitations of the study, in the construction of the models to predict BRAF