Abstract
ObjectiveMultiple mechanical learning models were used to predict the therapeutic dose of 131I radionuclide in patients with hyperthyroidism, and to compare the calculation results of each prediction model to obtain the optimal model for dose prediction. Meanwhile, the classification model was used to classify the prognosis of the existing clinical hyperthyroidism case data in order to evaluate the administration results and provide reference for the dose given by clinicians.MethodsAccording to the data of hyperthyroidism patients treated with 131I in nuclear medicine department of many hospitals, a prediction model was established based on MATLAB. Firstly, the prediction results of BP neural network, radial basis function (RBF) neural network and support vector machine (SVM) were compared with small sample data, and then the optimal model was selected to predict the drug dose. BP-AdaBoost, SVM and random forest were used to classify the patients after recovery and evaluate whether the dose was accurate.ResultsThe average errors of BP neural network, RBF neural network and SVM models trained with small samples were 6.58%, 17.25% and 14.09% respectively. After comparison, BP neural network was selected to establish the prediction model. The data of 30 cases were randomly selected to verify BP neural network, and average error of the prediction results was 11.99%. Using SVM, BP-AdaBoost and random forest models, 100 groups of case data were selected as the training set and 10 groups as the test set. The classification accuracy were 80%, 90% and 100% respectively. The random forest model with the highest accuracy was selected as the large sample prediction. When 318 groups of cases were trained and 35 groups of cases were used for the test, the classification accuracy was 97.14%.ConclusionThis study compared the prediction effects of various prediction models on 131I therapeutic dose in patients with hyperthyroidism and the accuracy of prognosis classification. BP neural network and random forest achieved the best results respectively. The two models provide reference for clinicians when giving the dose, which has clinical practical significance.
Data Sensitivity Measurement and Classification Model of Power IOT based on Information Entropy and BP Neural Network
