Survival Prediction Analysis Of Gastric Signet Ring Cell Carcinoma Based On Machine Learning Metho

This study utilized clinical data from a substantial sample of patients diagnosed with gastric signet ring cell carcinoma and employed machine learning algorithms to establish a predictive model for survival outcomes.Through a classification and regression approach,the model predicted treatment outcomes and survival periods for these patients,and its accuracy and reliability were validated under varying clinical characteristics.The findings offer important insights and support for the treatment and survival prediction of signet ring cell carcinoma patients and represent a noteworthy achievement in the application of classification and regression models.To begin,the study acquired the data of gastric signet ring cell carcinoma patients from the SEER database.The primary tasks involved in the data preprocessing and exploratory analysis included identifying missing,unknown,and outlier values,conducting data feature transformation and selection,and splitting the dataset.These steps were crucial in ensuring the accuracy and reliability of the subsequent analysis.Furthermore,considering the dataset features,research objectives,and algorithm characteristics,the study selected decision tree,random forest,support vector machine,and BP neural network models for classification prediction.By comparing the prediction results of these models on the test set,the study found that the BP neural network model had the best performance,achieving an accuracy rate of 82%,a recall rate of 79%,and excellent generalization ability.These findings underscore the potential of machine learning methods in predicting the survival and cure rates of gastric signet ring cell carcinoma patients.Moreover,the study constructed survival analysis regression models for both AFT and Cox regression based on the training set.By evaluating indicators such as the C-index and AIC values,the study found that the Cox regression model had good predictive capability.Next,the study used the optimal regression model to produce a column chart that visualized the model results.The evaluation of the model using various indicators,including the C-index and calibration chart,showed that the evaluation results aligned with the ideal.These findings suggest that the column chart produced by the Cox model could effectively predict the survival and cure rates of gastric signet ring cell carcinoma patients.Additionally,the study employed the Kaplan-Meier survival analysis algorithm to draw survival curves that explored factors influencing the probability of patient survival from both single and multiple perspectives.The findings revealed that age,TNM staging,and surgery were significant factors impacting the probability of patient survival.These results have important implications for developing effective treatment strategies and improving patient outcomes.