Cardiovascular disease, stroke and cirrhosis are diseases that pose a major health threat worldwide. This study carries out data-driven disease risk prediction and association analysis based on three disease datasets. In the first step, the stroke, heart disease and cirrhosis datasets were systematically preprocessed, including outlier processing based on the K-S test, spline function interpolation of missing values, and standardization and visual analysis. Through correlation analysis and chi-square test, the key influencing factors such as age, ST-segment depression, and albumin were identified. In the second step, a logistic regression-XGBoost integrated model was constructed to predict the prevalence probability of three types of diseases, and the model performance was evaluated through accuracy, AUC-ROC and other indicators, among which the accuracy of logistic regression for heart disease prediction reached 68%, and XGBoost's performance in the multi-classification task of liver cirrhosis needs to be improved. The results showed that the probability of heart disease-cirrhosis complications reached 83.33% in the high-risk group and 25.75% in the high-risk group, revealing a strong correlation between diseases. This study provides data support and method reference for multi-disease risk prediction and collaborative prevention and control.

Yihao Zhu, Ruixin Zhang, Junfeng Li. Integrated Logistic Regression–XGBoost and Bayesian Network Models for Disease Prediction. Academic Journal of Computing & Information Science (2026), Vol. 9, Issue 8: 113-117. https://doi.org/10.25236/AJCIS.2026.090814.

[1] Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794.

[2] Koller, D., & Friedman, N. (2009). Probabilistic graphical models: principles and techniques. MIT press.

[3] Pedregosa, F., et al. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825–2830.

[4] D’Agostino, R. B., et al. (2008). General cardiovascular risk profile for use in primary care: the Framingham Heart Study. Circulation, 117(6), 743–753.

[5] Kamath, P. S., et al. (2001). A model to predict survival in patients with end-stage liver disease. Hepatology, 33(2), 464–470.

[6] James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning. Springer.

[7] Friedman, J., Hastie, T., & Tibshirani, R. (2000). Additive logistic regression: a statistical view of boosting. Annals of statistics, 28(2), 337–407.

[8] Breiman, L. (2001). Random forests. Machine learning, 45(1), 5–32.

[9] Hosmer Jr, D. W., Lemeshow, S., & Sturdivant, R. X. (2013). Applied logistic regression. John Wiley & Sons.

[10] Pearl, J. (1988). Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan Kaufmann.

[11] Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: data mining, inference, and prediction. Springer.

[12] LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.

[13] Liu, Y., et al. (2020). A deep learning system for differential diagnosis of skin diseases. Nature medicine, 26(6), 900–908.

[14] Esteva, A., et al. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115–118.

[15] Rajkomar, A., et al. (2018). Scalable and accurate deep learning with electronic health records. NPJ Digital Medicine, 1(1), 18.

[16] Weng, S. F., et al. (2017). Can machine-learning improve cardiovascular risk prediction using routine clinical data? PloS one, 12(4), e0174944.

[17] Deo, R. C. (2015). Machine learning in medicine. Circulation, 132(20), 1920–1930.

[18] Obermeyer, Z., & Emanuel, E. J. (2016). Predicting the future—big data, machine learning, and clinical medicine. The New England journal of medicine, 375(13), 1216.

[19] Goldstein, B. A., et al. (2017). Opportunities and challenges in developing risk prediction models with electronic health records data: a systematic review. Journal of the American Medical Informatics Association, 24(1), 198–208.

[20] Shickel, B., et al. (2018). Deep EHR: a survey of recent advances in deep learning techniques for electronic health record (EHR) analysis. IEEE journal of biomedical and health informatics, 22(5), 1589–1604.