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December 6, 2024MedicineOpen Access

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

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Key result

Age ≥ 70 years was strongly associated with increased cardiovascular disease risk (AOR 3.963) compared to ages 18-34, and machine learning models predicted risk with up to 80.79% AUC.

Why the study?

To enhance understanding of the influence of type-2 diabetes and underlying determinants on cardiovascular disease risk, and construct precise predictive models in Bangladesh.

Population

Individuals with hypertension from the 2011 and 2017 to 2018 Bangladesh Demographic and Health Surveys

Comparison

Eight machine learning algorithms compared across 6 evaluation metrics

Design

Cross-sectional survey-based predictive modeling study

Authors

SDShatabdi DasKhulna UniversityRRRiaz RahmanKhulna UniversityATAshis TalukderAustralian National University

Discussion

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Member takes

Overview

May inform local CVD risk stratification in hypertensive Bangladeshi patients with diabetes; leaves open external validation in prospective cohorts.

Study Design

Type

Observational

Structured PICO

P
Population
Individuals with hypertension from the 2011 and 2017-2018 Bangladesh Demographic and Health Surveys assessed for cardiovascular disease risk determinants.
O
Outcome
Cardiovascular disease risk prediction performance (accuracy, AUC, specificity)

Main Result

Odds Ratio: 3.963

Machine learning models, particularly Random Forest, can accurately predict CVD risk in hypertensive individuals in Bangladesh, highlighting age, wealth, and BMI as key determinants.

Cite This Study

Das et al. (2024) conducted an observational in Hypertension and cardiovascular disease risk. Age ≥ 70 years vs. Age 18 to 34 years was evaluated on Cardiovascular disease (CVD) risk (AOR 3.963). Age ≥ 70 years was strongly associated with increased cardiovascular disease risk (AOR 3.963) compared to ages 18-34, and machine learning models predicted risk with up to 80.79% AUC.

synapsesocial.com/papers/6a6f530ffebe604dd7084866https://doi.org/10.1097/md.0000000000040813
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Determinants of Developing Cardiovascular Disease Risk with Emphasis on Type-2 Diabetes and Predictive Modeling Utilizing Machine Learning Algorithms2024
  2. 2Application of machine learning approaches to develop predictive models for diabetes and hypertension among Bangladeshi Adults2026 · 1 citations
  3. 3Leveraging Machine Learning to Assess Risk of Type 2 Diabetes and Cardiovascular Diseases in the North Indian Cohort2026
  4. 4Machine learning and deep learning–based prediction of hypertension and analysis of its major risk factors in Bangladesh2026
  5. 5Development and validation of a machine learning model for cardiovascular disease risk prediction in type 2 diabetes patients2025 · 17 citations