Development and validation of machine learning models predicting hospitalizations of hypertensive patients over 12 months

Aim . To develop models for predicting hospitalizations of hypertensive (HTN) over 12 months using machine learning algorithms and to validate them using real-world practice data. Material and methods . Based on the data from depersonalized electronic health records obtained from the Webiomed platform, 1165770 records of 151492 patients with HTN were selected. After the initial selection, a total of 43 anamnestic, constitutional, clinical, and paraclinical features were used as predictors. Automatic machine learning tools were used to create the models. A wide range of algorithms was considered, including logistic regression, decision tree-based methods using gradient boosting and bagging, discriminant analysis, a neural network algorithm and a naive Bayes classifier. Data from a single region were used for external validation. Results . The XGBoost model showed the best results, achieving an area under the ROC curve (AUC) of 0,849 (95% confidence interval: 0,825-0,873) during internal testing and 0,815 (95% confidence interval: 0,797-0,835) during external validation. Conclusion . A new highly accurate model for predicting hospitaliza­tion of HTN patients based on real-world data was developed. The results of external validation of the final model showed relative re­sistance to new data from another region that in combination with quality metrics presents the possibility of its approval for application in clinical practice.