Artificial intelligence improves risk prediction in cardiovascular disease

Cardiovascular disease (CVD) represents a major public health issue, claiming numerous lives. This study aimed to demonstrate the advantages of employing artificial intelligence (AI) models to improve the prediction of CVD risk using a large cohort of relatively healthy adults aged 70 years or more. In this study, deep learning (DL) models provide enhanced predictions (DeepSurv: C-index = 0.662, Integrated Brier Score (IBS) = 0.046; Neural Multi-Task Logistic Regression (NMTLR): C-index = 0.660, IBS = 0.047), as compared to the conventional (Cox: C-index = 0.634, IBS = 0.048) and machine learning (Random Survival Forest (RSF): C-index = 0.641, IBS = 0.048) models. The risk scores generated by the DL models also demonstrated superior performance. Moreover, AI models (NMTLR, DeepSurv, and RSF) were more effective, requiring the treatment of only 9 to 10 patients to prevent one CVD event, compared to the conventional model requiring treatment of nearly four times higher number of patients (NNT = 38). In summary, AI models, particularly DL models, possess superior predictive capabilities that can enhance patient treatment in a more cost-effective manner. Nonetheless, AI tools should serve to complement and assist healthcare professionals, rather than supplant them. The DeepSurv model, selected due to its relatively superior performance, is deployed in the form of web application locally, and is accessible on GitHub (https://github.com/Robidar/Chuchu_Depl). Finally, as we have demonstrated the benefit of using AI for reassessment of an existing CVD risk score, we recommend other infamous risk scores undergo similar reassessment.