Development and validation of a machine learning model for online predicting the risk of in heart failure: based on the routine blood test and their derived parameters

We used an online predictive tool to develop a predictive machine-learning model. The main purpose of this model was to predict the probability of developing HF in the future. This prediction can provide strong support and references for clinicians when making decisions. This online forecasting tool not only processes a large amount of data but also continuously optimizes and adjusts the accuracy of the model according to the latest medical research and clinical data. We hope to identify high-risk patients for early intervention to reduce the incidence of HF and improve their quality of life.