Cardiovascular Risk Prediction Dataset from Bangladeshi Survey Data with Demographic, Behavioral, Lifestyle, and Clinical Features

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Authors: Md. Mostafijur Rahman, MD Sabbir Hossain, Md. Tarikul Islam, MD Sozon
Cited by: 0

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