Loading article...
Loading article...
Stream/Area:
-
Subject:
-
Paper ID:
IJSREM64185
Volume:
10 |
5 |
May 2026
Pages:
1-7
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

Computer Science and Engineering
Ashokrao Mane Group of Institutions, Vathar

Computer Science and Engineering
Ashokrao Mane Group of Institutions, Vathar

Computer Science and Engineering
Ashokrao Mane Group of Institutions, Vathar

Computer Science and Engineering
Ashokrao Mane Group of Institutions, Vathar

Computer Science and Engineering
Ashokrao Mane Group of Institutions, Vathar
Artificial Intelligence (AI) is transforming healthcare by enabling intelligent systems for disease prediction, personalized recommendations, and automated medical assistance. This paper presents an AI-Powered Healthcare Intelligence Network that integrates Machine Learning (ML), Natural Language Processing (NLP), Retrieval-Augmented Generation (RAG), and Large Language Models (LLMs) to provide comprehensive healthcare support. The proposed system includes Disease Prediction, Drug Recommendation, Heart Disease Risk Assessment, and an AI-powered Medical Chatbot (Medibot).\n\nThe Disease Prediction module uses Random Forest algorithms to identify diseases based on symptoms, while the Drug Recommendation System utilizes NLP and cosine similarity to suggest similar medicines. The Heart Disease module employs LightGBM and Explainable AI for cardiovascular risk analysis. Additionally, the Medibot integrates LangChain, FAISS vector databases, HuggingFace embeddings, and Groq- powered Llama models for contextual medical question answering.\n\nThe system enhances healthcare accessibility, delivers personalized recommendations, and provides AI-assisted medical guidance through an interactive Streamlit-based web application.
KEYWORDS
The AI-Powered Healthcare Intelligence Network successfully demonstrates the integration of Machine Learning, NLP, Explainable AI, and Retrieval-Augmented Generation for intelligent healthcare assistance. The system combines disease prediction, drug recommendation, heart disease risk assessment, and AI-powered medical chatbot functionalities into a unified healthcare platform. The proposed application enhances healthcare accessibility, delivers personalized medical guidance, and demonstrates the practical implementation of AI technologies in healthcare systems. The integration of LangChain, FAISS vector databases, and Large Language Models further strengthens the system by enabling contextual medical conversations and intelligent information retrieval. The project highlights the growing importance of Artificial Intelligence in modern healthcare and provides a scalable foundation for future AI- driven healthcare applications. .
[1] J. Breiman, “Random Forests,” Machine Learning, vol. 45, no. 1, pp. 5–32, 2001. [2] T. Mikolov et al., “Efficient Estimation of Word Representations in Vector Space,” arXiv preprint arXiv:1301.3781, 2013. [3] HuggingFace, “Sentence Transformers Documentation,” 2024. [4] LangChain Documentation, “Building Applications with LLMs through Composability,” 2024. [5] Facebook AI Research, “FAISS: A Library for Efficient Similarity Search,” 2024. [6] Groq Documentation, “Groq API and Llama Model Integration,” 2024. [7] T. Chen and C. Guestrin, “XGBoost: A Scalable Tree Boosting System,” KDD, 2016. [8] Ke et al., “LightGBM: A Highly Efficient Gradient Boosting Decision Tree,” NeurIPS, 2017. [9] Lundberg and Lee, “A Unified Approach to Interpreting Model Predictions,” NeurIPS, 2017. [10] Streamlit Documentation, “Data Apps and AI Applications,” 2024. [11] Python Software Foundation, “Python Documentation,” 2024. [12] McKinney, W., “Data Structures for Statistical Computing in Python,” Proceedings of the Python in Science Conference, 2010
Publication Date
5/28/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
Documents
Article Views
0
Total views since publication
PDF Downloads
3
Total downloads since publication
Geographic Reach
5
Digital Preservation: This article is permanently archived on Zenodo and complies with all data preservation standards across major indexing platforms.
Plagiarism Screening
This manuscript has been checked for plagiarism using TurnitinPlagiarismChecker software.
Conflict of Interest
The authors declare no conflict of interest related to this research work.
Funding Information
No funding information available for this research.
Related research in the same subject area
Loading similar articles...