CoChat
Sign Up
Cited by 7Artificial Intelligence in HealthcareDiabetes, Cardiovascular Risks, and LipoproteinsCardiovascular Health and Risk Factors

Determinants of developing cardiovascular disease risk with emphasis on type-2 diabetes and predictive modeling utilizing machine learning algorithms

Open access
Das, Shatabdi, Rahman, Riaz, Talukder, Ashis·2024·Medicine
DOI 10.1097/md.0000000000040813PMC PMC11630972
Open this paper in CoChat — free account

Abstract

This research aims to enhance our comprehensive understanding of the influence of type-2 diabetes on the development of cardiovascular diseases (CVD) risk, its underlying determinants, and to construct precise predictive models capable of accurately assessing CVD risk within the context of Bangladesh. This study combined data from the 2011 and 2017 to 2018 Bangladesh Demographic and Health Surveys, focusing on individuals with hypertension. CVD development followed World Health Organization (WHO) guidelines. Eight machine learning algorithms (Support Vector Machine, Logistic Regression, Decision Tree, Random Forest, Naïve Bayes, K-Nearest Neighbor, Light GBM, and XGBoost) were analyzed and compared using 6 evaluation metrics to assess model performance. The study reveals that individuals aged 35 to 54 years, 55 to 69 years, and ≥ 70 years face higher CVD risk with adjusted odds ratios (AOR) of 2.140, 3.015, and 3.963, respectively, compared to those aged 18 to 34 years. “Rich” respondents show increased CVD risk (AOR = 1.370, P < .01) compared to “poor” individuals. Also, “normal weight” (AOR = 1.489, P < .01) and “overweight/obese” (AOR = 1.871, P < .01) individuals exhibit higher CVD risk than “underweight” individuals. The predictive models achieve impressive performance, with 75.21% accuracy and an 80.79% AUC, with Random Forest (RF) excelling in specificity at 76.96%. This research holds practical implications for targeted interventions based on identified significant factors, utilizing ML models for early detection and risk assessment, enhancing awareness and education, addressing urbanization-related lifestyle changes, improving healthcare infrastructure in rural areas, and implementing workplace interventions to mitigate stress and promote physical activity.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1097/md.0000000000040813

Cites

3 of 3 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 30

  1. [1]Jin Z Oresko J Huang S Cheng AC. HeartToGo: a personalized medicine technology for cardiovascular disease prevention and detection. In: 2009 IEEE/NIH Life Science Systems and Applications Workshop. IEEE; 2009: 80– 83.
  2. [2]Rahim A Rasheed Y Azam F Anwar MW Rahim MA Muzaffar AW. An integrated machine learning framework for effective prediction of cardiovascular diseases. IEEE Access. 2021; 9: 106575– 88.
  3. [3]Maiga J Hungilo GG. Comparison of machine learning models in prediction of cardiovascular disease using health record data. In: 2019 International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS). IEEE; 2019: 45– 48.
  4. [4]World Health Organization, & International Society of Hypertension Writing Group. 2003 World Health Organization (WHO)/International Society of Hypertension (ISH) statement on management of hypertension. J Hypertens. 2003; 21: 1983– 92. 14597836
  5. [5]Kaptoge S Pennells L De Bacquer D. World Health Organization cardiovascular disease risk charts: revised models to estimate risk in 21 global regions. Lancet Global Health. 2019; 7: e1332– 45. 31488387 PMC7025029
  6. [6]Keeley EC Boura JA Grines CL. Primary angioplasty versus intravenous thrombolytic therapy for acute myocardial infarction: a quantitative review of 23 randomised trials. Lancet. 2003; 361: 13– 20. 12517460
  7. [8]Romiti S Vinciguerra M Saade W Anso Cortajarena I Greco E. Artificial intelligence (AI) and cardiovascular diseases: an unexpected alliance. Cardiol Res Pract. 2020; 2020: 1– 8. PMC7336209 32676206
  8. [10]Lakshmanarao A Swathi Y Sundareswar PSS. Machine learning techniques for heart disease prediction. Forest. 2019; 95: 97.
  9. [11]Rahman M Nakamura K Seino K Kizuki M. Sociodemographic factors and the risk of developing cardiovascular disease in Bangladesh. Am J Prev Med. 2015; 48: 456– 61. 25498549
  10. [12]Nashif S Raihan MR Islam MR Imam MH. Heart disease detection by using machine learning algorithms and a real-time cardiovascular health monitoring system. World J Eng Technol. 2018; 06: 854– 73.
  11. [13]Sajid MR Muhammad N Zakaria R. Nonclinical features in predictive modeling of cardiovascular diseases: a machine learning approach. Interdiscip Sci Comput Life Sci. 2021; 13: 201– 11. 33675528
  12. [14]Alić B Gurbeta L Badnjević A. Machine learning techniques for classification of diabetes and cardiovascular diseases. In: 2017 6th Mediterranean Conference on Embedded Computing (MECO). IEEE; 2017: 1– 4.
  13. [15]Ruan Y Guo Y Zheng Y. Cardiovascular disease (CVD) and associated risk factors among older adults in six low-and middle-income countries: results from SAGE Wave 1. BMC Public Health. 2018; 18: 1– 13. PMC6011508 29925336
  14. [16]Garavand A Behmanesh A Aslani N Sadeghsalehi H Ghaderzadeh M. Towards diagnostic aided systems in coronary artery disease detection: a comprehensive multiview survey of the state of the art. Int J Intell Syst. 2023; 2023: 1– 19.
  15. [17]Garavand A Salehnasab C Behmanesh A Aslani N Zadeh AH Ghaderzadeh M. Efficient model for coronary artery disease diagnosis: a comparative study of several machine learning algorithms. J Healthcare Eng. 2022; 2022: 5359540. PMC9596250 36304749
  16. [18]Kumar NK Sindhu GS Prashanthi DK Sulthana AS. Analysis and prediction of cardio vascular disease using machine learning classifiers. In: 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS). IEEE; 2020: 15– 21.
  17. [19]Alizadehsani R Abdar M Roshanzamir M. Machine learning-based coronary artery disease diagnosis: a comprehensive review. Comput Biol Med. 2019; 111: 103346. 31288140
  18. [20]Baccouche A Garcia-Zapirain B Castillo Olea C Elmaghraby A. Ensemble deep learning models for heart disease classification: a case study from Mexico. Information. 2020; 11: 207.
  19. [21]Ene-Iordache B Perico N Bikbov B. Chronic kidney disease and cardiovascular risk in six regions of the world (ISN-KDDC): a cross-sectional study. Lancet Global Health. 2016; 4: e307– 19. 27102194
  20. [23]Tada H Fujino N Hayashi K Kawashiri M Takamura M. Human genetics and its impact on cardiovascular disease. J Cardiol. 2022; 79: 233– 9. 34551866
  21. [24]Chowdhury R Ramond A O’Keeffe LM. Environmental toxic metal contaminants and risk of cardiovascular disease: systematic review and meta-analysis. BMJ. 2018; 362: k3310. 30158148 PMC6113772
  22. [25]Chandralekha M Shenbagavadivu N. Performance analysis of various machine learning techniques to predict cardiovascular disease: an emprical study. Appl Math Inf Sci. 2018; 12: 217– 26.
  23. [26]Arunachalam S. Cardiovascular disease prediction model using machine learning algorithms. Int J Res Appl Sci Eng Technol. 2020; 8: 1006– 19.
  24. [28]Al Kibria GM Burrowes V Choudhury A Sharmeen A Swasey K. Sex differences in prevalence and associated factors of prehypertension and hypertension among Bangladeshi adults. Int J Cardiol Hypertens. 2019; 1: 100006. 33447740 PMC7803050
  25. [29]Cercato C Fonseca FA. Cardiovascular risk and obesity. Diabetol Metab Syndr. 2019; 11: 74. 31467596 PMC6712750
  26. [30]Rodgers JL Jones J Bolleddu SI. Cardiovascular risks associated with gender and aging. J Cardiovasc Dev Dis. 2019; 6: 19. 31035613 PMC6616540
  27. [31]Thompson SC Nedkoff L Katzenellenbogen J Hussain MA Sanfilippo F. Challenges in managing acute cardiovascular diseases and follow up care in rural areas: a narrative review. Int J Environ Res Public Health. 2019; 16: 5126. 31847490 PMC6950682
  28. [32]Clark AM DesMeules M Luo W Duncan AS Wielgosz A. Socioeconomic status and cardiovascular disease: risks and implications for care. Nat Rev Cardiol. 2009; 6: 712– 22. 19770848
  29. [33]Erem C Hacihasanoglu A Kocak M Deger O Topbas M. Prevalence of prehypertension and hypertension and associated risk factors among Turkish adults: Trabzon hypertension study. J Public Health (Oxf). 2009; 31: 47– 58. 18829520
  30. [34]Petrelli A Sebastiani G Napoli AD. Education inequalities in cardiovascular and coronary heart disease in Italy and the role of behavioral and biological risk factors. Nutr Metab Cardiovasc Dis. 2022; 32: 918– 28. 35067447

Cited by

Sorted by citations

Authors
Das, S., Rahman, R., & Talukder, A.
Affiliations
  • Khulna University (BD)
  • Australian National University (AU)
Corresponding author
Talukder, Ashis
ORCID
Venue
Medicine
Publisher
Ovid Technologies (Wolters Kluwer Health)
Published
2024-12-06
Volume
103
Issue
49
Pages
e40813
ISSN
  • 0025-7974 (ISSN-L)
  • 1536-5964
DOI
10.1097/md.0000000000040813
PMC
PMC11630972
OpenAlex
W4405171464
Cited by
7
References
3
Citations by year
2026: 2 · 2025: 5
FWCI
2.14
Type
journal article
Language
en
Open access
Licence
Locations
  • Medicine (publishedVersion, cc-by)
  • Research Square (acceptedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Topics
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
  • Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes, Cardiovascular Risks, and Lipoproteins
  • Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Cardiovascular Health and Risk Factors
Keywords
cardiovascular risk, type 2 diabetes, hypertension, risk factors, Bangladesh, age, wealth status, body mass index, obesity, overweight, random forest, machine learning prediction, Bangladesh Demographic and Health Survey
MeSH terms
Machine Learning, Adolescent, Adult, Aged, Algorithms, Bangladesh, Cardiovascular Diseases, Diabetes Mellitus, Type 2, Female, Humans, Hypertension, Male, Middle Aged, Risk Factors, Socioeconomic Factors, Risk Assessment, Young Adult

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review