Development and validation of an artificial intelligence-based model for cardiovascular disease prediction using longitudinal data | BMC Medical Informatics and Decision Making

Taheri Soodejani M. Non-communicable diseases in the world over the past century: a secondary data analysis. Front Public Health. 2024;12:1436236.

Google Scholar 

Roth GA, Mensah GA, Johnson CO, Addolorato G, Ammirati E, Baddour LM, et al. Global burden of cardiovascular diseases and risk factors, 1990–2019: update from the GBD 2019 study. J Am Coll Cardiol. 2020;76(25):2982–3021.

Google Scholar 

Aminorroaya A, Yoosefi M, Rezaei N, Shabani M, Mohammadi E, Fattahi N, et al. Global, regional, and National quality of care of ischaemic heart disease from 1990 to 2017: a systematic analysis for the global burden of disease study 2017. Eur J Prev Cardiol. 2022;29(2):371–9.

Google Scholar 

Organization WH. Noncommunicable diseases country profiles. 2018 [acceso: 30/07/2019].

Rahmani A, Sayehmiri K, Asadollahi K, Sarokhani D, Islami F, Sarokhani M. Investigation of the prevalence of obesity in Iran: a systematic review and meta-analysis study. Acta Medica Iranica. 2015;596–607.

Uthman OA. Global, regional, and National disability-adjusted life years (DALYs) for 315 diseases and injuries and healthy life expectancy (HALE) for 195 countries and territories, 1990‐2015: a systematic analysis for the global burden of Diseases, Injuries, and risk factors (GBD) 2015 study. Lancet. 2016;388(10053):1603–58.

Google Scholar 

Roth GA, Johnson C, Abajobir A, Abd-Allah F, Abera SF, Abyu G, et al. Global, regional, and National burden of cardiovascular diseases for 10 causes, 1990 to 2015. J Am Coll Cardiol. 2017;70(1):1–25.

Google Scholar 

Danaei G, Farzadfar F, Kelishadi R, Rashidian A, Rouhani OM, Ahmadnia S, et al. Iran in transition. Lancet. 2019;393(10184):1984–2005.

Google Scholar 

Mensah GA, Brown DW. An overview of cardiovascular disease burden in the united States. Health Aff. 2007;26(1):38–48.

Google Scholar 

Jabir R, Nasir Siddiqui N, Kandy Firoz A, Md Ashraf C, Kashif Zaidi G, Shahnawaz Khan S. Current updates on therapeutic advances in the management of cardiovascular diseases. Curr Pharm Design. 2016;22(5):566–71.

Google Scholar 

Guidry UC, Evans JC, Larson MG, Wilson PW, Murabito JM, Levy D. Temporal trends in event rates after Q-wave myocardial infarction: the Framingham heart study. Circulation. 1999;100(20):2054–9.

Google Scholar 

Piepoli MF, Hoes AW, Agewall S, Albus C, Brotons C, Catapano AL, et al. Guidelines: editor’s choice: 2016 European guidelines on cardiovascular disease prevention in clinical practice: the sixth joint task force of the European society of cardiology and other societies on cardiovascular disease prevention in clinical practice (constituted by representatives of 10 societies and by invited experts) developed with the special contribution of the European association for cardiovascular prevention & rehabilitation (EACPR). Eur Heart J. 2016;37(29):2315.

Google Scholar 

Liu S, Li Y, Zeng X, Wang H, Yin P, Wang L, et al. Burden of cardiovascular diseases in China, 1990–2016: findings from the 2016 global burden of disease study. JAMA Cardiol. 2019;4(4):342–52.

Google Scholar 

Goff DC Jr, Lloyd-Jones DM, Bennett G, Coady S, D’agostino RB, Gibbons R, et al. 2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American college of Cardiology/American heart association task force on practice guidelines. Circulation. 2014;129(25suppl2):S49–73.

Google Scholar 

Faizal ASM, Thevarajah TM, Khor SM, Chang S-W. A review of risk prediction models in cardiovascular disease: conventional approach vs. artificial intelligent approach. Comput Methods Programs Biomed. 2021;207:106190.

Google Scholar 

Azizi F, Madjid M, Rahmani M, Emami H, Mirmiran P, Hadjipour R. Tehran lipid and glucose study (TLGS): rationale and design. Iran J Endocrinol Metabolism. 2000;2(2):77–86.

Google Scholar 

Azizi F, Ghanbarian A, Momenan AA, Hadaegh F, Mirmiran P, Hedayati M, et al. Prevention of non-communicable disease in a population in nutrition transition: Tehran lipid and glucose study phase II. Trials. 2009;10:1–15.

Google Scholar 

Rajendran A, Minhas AS, Kazzi B, Varma B, Choi E, Thakkar A, et al. Sex-specific differences in cardiovascular risk factors and implications for cardiovascular disease prevention in women. Atherosclerosis. 2023;384:117269.

Google Scholar 

Arunachalam S. Cardiovascular disease prediction model using machine learning algorithms. Int J Res Appl Sci Eng Technol. 2020;8:1006–19.

Google Scholar 

Choi E, Schuetz A, Stewart WF, Sun J. Using recurrent neural network models for early detection of heart failure onset. J Am Med Inform Assoc. 2017;24(2):361–70.

Google Scholar 

Sung JM, Cho I-J, Sung D, Kim S, Kim HC, Chae M-H, et al. Development and verification of prediction models for preventing cardiovascular diseases. PLoS ONE. 2019;14(9):e0222809.

Google Scholar 

Alaa AM, Bolton T, Di Angelantonio E, Rudd JH, Van der Schaar M. Cardiovascular disease risk prediction using automated machine learning: A prospective study of 423,604 UK biobank participants. PLoS ONE. 2019;14(5):e0213653.

Google Scholar