Evaluating the Effectiveness of Health Disease Prediction Using Ensemble Learning

A. A. Babar, R. V. Argiddi, M. A. Mahant

2025

Abstract

In today's world, the healthcare sector is vast and faces numerous challenges, especially in rural areas where access to timely and affordable medical consultations is often limited. High costs, time constraints, and a shortage of healthcare professionals in remote regions hinder early disease detection, diagnosis, and treatment, which can lead to serious health complications. Recent advancements in technology have opened up new possibilities for innovative solutions, including healthcare chatbots. However, current chatbot systems encounter issues such as inaccurate predictions, a lack of contextual understanding, limited adaptability to user preferences, and concerns about data privacy. The proposed system aims to tackle these challenges by utilizing advanced NLP and ML algorithms to develop an intelligent healthcare chatbot capable of real-time disease prediction. By assessing user symptoms, medical history, and lifestyle factors, the chatbot can offer preliminary diagnoses, personalized health recommendations, and guide users to appropriate medical consultations. This system provides immediate assistance, alleviates the burden on healthcare professionals, and enhances patient care management by effectively distinguishing between critical and non-critical cases.

Paper Citation

in Harvard Style

Babar A., Argiddi R. and Mahant M. (2025). Evaluating the Effectiveness of Health Disease Prediction Using Ensemble Learning. In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25; ISBN 978-989-758-777-1, SciTePress, pages 389-397. DOI: 10.5220/0013883500004919

in Bibtex Style

@conference{icrdicct`2525,
author={A. Babar and R. Argiddi and M. Mahant},
title={Evaluating the Effectiveness of Health Disease Prediction Using Ensemble Learning},
booktitle={Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25},
year={2025},
pages={389-397},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013883500004919},
isbn={978-989-758-777-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25
TI - Evaluating the Effectiveness of Health Disease Prediction Using Ensemble Learning
SN - 978-989-758-777-1
AU - Babar A.
AU - Argiddi R.
AU - Mahant M.
PY - 2025
SP - 389
EP - 397
DO - 10.5220/0013883500004919
PB - SciTePress