LAUSR

Traditional algorithms have the following drawbacks: (1) they only focus on a certain aspect of genetic data or local feature data of osteosarcoma patients, and the extracted feature information is not considered as a whole; (2) they do not equalize the sample data between categories; (3) the generalization ability of the model is weak, and it is difficult to perform the task of classifying the survival status of osteosarcoma patients better. In this context, this paper designs a survival status prediction model for osteosarcoma patients based on E-CNN-SVM and multisource data fusion, taking into full consideration the characteristics of the small number of samples, high dimensionality, and interclass imbalance of osteosarcoma patients' genetic data. The model fuses four gene sequencing data highly correlated with bone tumors using the random forest algorithm in a dimensionality reduction and then equalizes the data using a hybrid sampling method combining the SMOTE algorithm and the TomekLink algorithm; secondly, the CNN model with the incentive module is used to further extract features from the data for more accurate extraction of characteristic information; finally, the data are passed to the SVM model to further improve the stability and classification performance of the model. The model has been demonstrated to be more effective in improving the accuracy of the classification of patients with osteosarcoma.