BL-MVC: Blockchain Enabled Majority Voting Classifier for Predicting Heart Diseases

Deepa Kumari, Akshat Kumar K., Ashutosh Wagh, S. Shashank, Abhishek Patidar, Subhrakanta Panda

2025

Abstract

This paper introduces an innovative framework merging Block-chain and a Majority Voting Classifier (MVC) for heart disease detection, aiming to enhance security and accuracy in managing Electronic Health Records (EHR). The proposed system leverages Blockchain’s distributed ledger and smart contract capabilities to create a secure, tamper-resistant repository for heart-related patient data. The architecture comprises a user-friendly React-based front-end and a FastAPI-powered back-end, interfacing with a local blockchain like Ganache. Solidity smart contracts ensure transparent and secure storage of patient responses, which the framework analyzes through various machine learning models, including hyper-tuned LR, MLP, AdaBoost, CatBoost, and XGBoost. The proposed approach ensembles the prediction using MVC and achieves diagnostic accuracy up to 90%. This paper also compares machine learning models’ performance using evaluation metrics such as accuracy, sensitivity, specificity, precision, F1-measure, Matthew correlation coefficient (MCC), and ROC curve. This integrated framework can empower physicians to diagnose heart disease patients while safeguarding sensitive health data accurately.

Paper Citation

in Harvard Style

Kumari D., K. A., Wagh A., Shashank S., Patidar A. and Panda S. (2025). BL-MVC: Blockchain Enabled Majority Voting Classifier for Predicting Heart Diseases. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-758-737-5, SciTePress, pages 45-56. DOI: 10.5220/0013096800003890

in Bibtex Style

@conference{icaart25,
author={Deepa Kumari and Akshat K. and Ashutosh Wagh and S. Shashank and Abhishek Patidar and Subhrakanta Panda},
title={BL-MVC: Blockchain Enabled Majority Voting Classifier for Predicting Heart Diseases},
booktitle={Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2025},
pages={45-56},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013096800003890},
isbn={978-989-758-737-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
TI - BL-MVC: Blockchain Enabled Majority Voting Classifier for Predicting Heart Diseases
SN - 978-989-758-737-5
AU - Kumari D.
AU - K. A.
AU - Wagh A.
AU - Shashank S.
AU - Patidar A.
AU - Panda S.
PY - 2025
SP - 45
EP - 56
DO - 10.5220/0013096800003890
PB - SciTePress