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Paper ID:IJSREM64185

AI-Powered Diagnosis and Healthcare Recommendation System to Enhance Patient Care

Stream/Area:

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Subject:

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Paper ID:

IJSREM64185

Volume:

10 |

5 |

May 2026

Pages:

1-7

Publication Frequency:

Monthly (12 issues per year)

DOI
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Not assigned

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.

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Authors

Hemant Gajanan Khadake

Hemant Gajanan Khadake

Computer Science and Engineering

Ashokrao Mane Group of Institutions, Vathar

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Trupti Surendra Chavan

Trupti Surendra Chavan

Computer Science and Engineering

Ashokrao Mane Group of Institutions, Vathar

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Tejas Gajanan Kharshinge

Tejas Gajanan Kharshinge

Computer Science and Engineering

Ashokrao Mane Group of Institutions, Vathar

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Shreya Vishwas Gurav

Shreya Vishwas Gurav

Computer Science and Engineering

Ashokrao Mane Group of Institutions, Vathar

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Sahil Sunil Kulkarni

Sahil Sunil Kulkarni

Computer Science and Engineering

Ashokrao Mane Group of Institutions, Vathar

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Abstract

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

Artificial Intelligence Healthcare Machine Learning Disease Prediction NLP LangChain Retrieval-Augmented Generation FAISS Medical Chatbot Streamlit Recommendation System.

Cite This Article

Hemant Gajanan Khadake, Trupti Surendra Chavan, Tejas Gajanan Kharshinge, Shreya Vishwas Gurav, Sahil Sunil Kulkarni, "AI-Powered Diagnosis and Healthcare Recommendation System to Enhance Patient Care," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 5, pp. 1-7, May 2026.

Full Article/Conclusion

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. .

References

[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

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Publication Date

5/28/2026

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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