Cardiovascular Risk Prediction Dataset from Bangladeshi Survey Data with Demographic, Behavioral, Lifestyle, and Clinical Features
Abstract
This dataset contains survey-based health data collected from 328 individuals, capturing a comprehensive set of demographic, lifestyle, clinical, and behavioral risk factors associated with heart disease. Each record represents one respondent and includes self-reported information on physical health indicators, daily habits, medical history, and a binary label indicating whether the individual is a heart patient. Sensitive or identifying information has been removed from the final released dataset to ensure privacy and prevent data leakage. The dataset can be used for binary CVD risk prediction, classification benchmarking, and explainable machine learning research. Keywords: Cardiovascular Risk Prediction, Survey-Based Dataset, Machine Learning, Demographic Features, Lifestyle Factors, Clinical Data
Fields of Study & Subjects
Artificial Intelligence in Healthcare
Cardiovascular Health and Risk Factors
Health Promotion and Cardiovascular Prevention
Publication details
- Journal / source
- Zenodo (CERN European Organization for Nuclear Research)
- Publication date
- 13 March 2026
- Language
- EN
- Document type
- Dataset