Author:
Chowdhury Mohammad Ziaul Islam,Leung Alexander A.,Walker Robin L.,Sikdar Khokan C.,O’Beirne Maeve,Quan Hude,Turin Tanvir C.
Abstract
AbstractRisk prediction models are frequently used to identify individuals at risk of developing hypertension. This study evaluates different machine learning algorithms and compares their predictive performance with the conventional Cox proportional hazards (PH) model to predict hypertension incidence using survival data. This study analyzed 18,322 participants on 24 candidate features from the large Alberta’s Tomorrow Project (ATP) to develop different prediction models. To select the top features, we applied five feature selection methods, including two filter-based: a univariate Cox p-value and C-index; two embedded-based: random survival forest and least absolute shrinkage and selection operator (Lasso); and one constraint-based: the statistically equivalent signature (SES). Five machine learning algorithms were developed to predict hypertension incidence: penalized regression Ridge, Lasso, Elastic Net (EN), random survival forest (RSF), and gradient boosting (GB), along with the conventional Cox PH model. The predictive performance of the models was assessed using C-index. The performance of machine learning algorithms was observed, similar to the conventional Cox PH model. Average C-indexes were 0.78, 0.78, 0.78, 0.76, 0.76, and 0.77 for Ridge, Lasso, EN, RSF, GB and Cox PH, respectively. Important features associated with each model were also presented. Our study findings demonstrate little predictive performance difference between machine learning algorithms and the conventional Cox PH regression model in predicting hypertension incidence. In a moderate dataset with a reasonable number of features, conventional regression-based models perform similar to machine learning algorithms with good predictive accuracy.
Publisher
Springer Science and Business Media LLC
Reference68 articles.
1. World Health Organization. Global Status Report on noncommunicable diseases 2014—Quot; Attaining the nine global noncommunicable diseases targets; a shared responsibility & quot (WHO, 2014).
2. Zhou, B. et al. Worldwide trends in hypertension prevalence and progress in treatment and control from 1990 to 2019: A pooled analysis of 1201 population-representative studies with 104 million participants. Lancet 398(10304), 957–980. https://doi.org/10.1016/S0140-6736(21)01330-1 (2021).
3. Zhou, B., Perel, P., Mensah, G. A. & Ezzati, M. Global epidemiology, health burden and effective interventions for elevated blood pressure and hypertension. Nat. Rev. Cardiol. 18(11), 785–802. https://doi.org/10.1038/s41569-021-00559-8 (2021).
4. The effects of hypertension on the body. Accessed January 2, 2021. https://www.healthline.com/health/high-blood-pressure-hypertension/effect-on-body
5. Ahmed, I., Debray, T. P., Moons, K. G. & Riley, R. D. Developing and validating risk prediction models in an individual participant data meta-analysis. BMC Med. Res. Methodol. https://doi.org/10.1186/1471-2288-14-3 (2014).
Cited by
4 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献