Survival prediction models for people living with HIV based on four machine learning models

Although antiretroviral therapy has prolonged the lifespan of people living with HIV, significant variations still exist in survival rates and risk factors among these people. This study compares the performance of the Cox proportional hazard models with four machine learning models in predicting the survival of people living with HIV, analyzing the survival factors among them, thereby assisting medical decision-making. We collected data on 676 people living with HIV from the Chinese Center for Disease Control and Prevention. Significant variables (p < 0.05) were identified using Cox univariate analysis. Using a random number method, the data were split into a training set (473 cases) and a test set (203 cases) in a 7:3 ratio. We employed the Cox proportional hazard model and four classification machine learning models, including eXtreme Gradient Boosting, Random Forest, Support Vector Machine, and Multilayer Perceptron, to develop survival prediction models for people living with HIV. The predictive performance of these models was evaluated based on accuracy, precision, recall, F1-score, area under the receiver operating characteristic curve (AUC), and calibration curves, and the best model was selected based on these metrics. The average age of diagnosis among the sample participants was 56.63 years (SD = 17.53). Considering the performance of both the training and testing cohorts, the Random Forest classifier emerged as the model with the best predictive performance, with an AUC of 0.912, an Accuracy of 0.862, a Precision of 0.794, a Recall of 0.562, and an F1 score of 0.659. Random Forest was followed by the Support Vector Machine, the eXtreme Gradient Boosting, Multilayer Perceptron, and the Cox proportional hazard model performed similarly. The predictive performance of machine learning models surpasses traditional Cox proportional hazard models. In China, the Random Forest model can be considered for analyzing and predicting the survival rates of people living with HIV.