Framingham risk score conventional risk factors are potent to predict all-cause mortality using machine learning...

Predicting all-cause mortality using available or conveniently modifiable risk factors is potentially crucial in reducing deaths precisely and efficiently. Framingham risk score (FRS) is widely used in predicting cardiovascular diseases, and its conventional risk factors are closely pertinent to deaths. Machine learning is increasingly considered to improve the predicting performances by developing predictive models. We aimed to develop the all-cause mortality predictive models using five machine learning (ML) algorithms (decision trees, random forest, support vector machine (SVM), XgBoost, and logistic regression) and determine whether FRS conventional risk factors are sufficient for predicting all-cause mortality in individuals over 40 years. Our data were obtained from a 10-year population-based prospective cohort study in China, including 9143 individuals over 40 years in 2011, and 6879 individuals followed-up in 2021. The all-cause mortality prediction models were developed using five ML algorithms by introducing all features available (182 items) or FRS conventional risk factors. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of the predictive models. The AUC and 95% confidence interval of the all-cause mortality prediction models developed by FRS conventional risk factors using five ML algorithms were 0.75 (0.726-0.772), 0.78 (0.755-0.799), 0.75 (0.731-0.777), 0.77 (0.747-0.792), and 0.78 (0.754-0.798), respectively, which is close to the AUC values of models established by all features (0.79 (0.769-0.812), 0.83 (0.807-0.848), 0.78 (0.753-0.798), 0.82 (0.796-0.838), and 0.85 (0.826-0.866), respectively). Therefore, we tentatively put forward that FRS conventional risk factors were potent to predict all-cause mortality using machine learning algorithms in the population over 40 years.