HealLeaf

Abstract

This study introduces HealLeaf, an intelligent healthcare chatbot designed to assist users in diagnosing potential illnesses through symptom analysis, offering preventative healthcare recommendations, and delivering information regarding appropriate medications. The platform incorporates a physician referral system utilizing a CSV-structured database containing healthcare provider information, including specializations, communication details, and practice locations. The illness identification component employs Random Forest methodology, developed and tested using medical information datasets. HealLeaf serves as a bridge between patients and medical practitioners by providing an accessible, efficient, and dependable preliminary healthcare support system. The algorithm demonstrates excellent accuracy, precision, and recall metrics, establishing its credibility as a supportive medical tool rather than a replacement for professional healthcare consultation. This document outlines the system framework, research approach, prediction processes, mathematical principles, algorithm enhancement, assessment criteria, and practical applications. The study emphasizes the growing significance of implementing advanced AI technologies in healthcare, particularly in environments where immediate access to professional medical services is restricted. The methodology adapts across diverse healthcare scenarios while enhancing both precision and dependability. Through incorporating contextual evaluation, enhanced data management, and ethical considerations including information security, the platform establishes patient confidence and participation. Upcoming enhancements will focus on multilingual capabilities, integration of regional healthcare patterns, and user interface optimization to develop a more comprehensive and efficient healthcare support system.:

Introduction

The proposed HealLeaf system is an AI-powered medical chatbot designed to provide instant healthcare assistance by predicting diseases from symptoms, suggesting preventive measures and medications, and connecting users to relevant doctors. It addresses barriers to healthcare access, especially in remote or underserved areas.

Unlike traditional chatbots, HealLeaf uses Natural Language Processing (NLP) and a Random Forest classifier for accurate disease prediction from multi-symptom inputs. The system offers a complete solution—from symptom collection to medical guidance—by integrating real-time symptom analysis, preventive advice, medicine recommendations, and doctor referrals through a user-friendly conversational interface.

Related Work

Existing chatbot solutions range from simple rule-based systems to advanced AI models. Many focus on:

• Symptom-based prediction (e.g., using Decision Trees, SVMs, or ANN)

• Privacy protection

• Accessibility in rural or resource-limited areas
  However, limitations persist, including low accuracy for rare conditions, high computational needs, and lack of real-time, integrated healthcare support.

HealLeaf Features

• NLP-based input processing (text; voice planned)

• Random Forest model for disease classification with high accuracy and confidence

• Integrated database of preventive measures, medicines, and doctors

• One-stop response with predicted disease, prevention, treatment info, and nearby doctors

Results

• High performance across metrics (accuracy, precision, recall, F1-score)

• Strong real-world validation, showing practical usability and reliability

• Efficient, accessible healthcare guidance via a single chatbot conversation

Conclusion

HealLeaf successfully demonstrates the integration of AI-based disease prediction, medicine guidance, and doctor connectivity in a unified chatbot system that addresses critical gaps in healthcare accessibility. The Random Forest model achieves high accuracy while maintaining interpretability, making it suitable for healthcare applications where explanation of results is crucial for user trust and medical transparency. By combining artificial intelligence with structured healthcare databases, the system provides both immediate medical information and actionable next steps for users, creating a comprehensive healthcare assistance platform that bridges the gap between symptom analysis and professional medical care. While the system cannot replace professional diagnosis and should not be considered a substitute for qualified medical consultation, HealLeaf serves as a valuable preliminary healthcare assistant that is especially beneficial in remote or underserved areas where immediate professional consultation may not be readily available. The research contributes to the growing field of AI-powered healthcare assistance by demonstrating how machine learning can be effectively combined with practical healthcare connectivity to create accessible, reliable, and user-friendly medical assistance tools. The system\'s comprehensive approach to medical assistance, from initial symptom analysis through professional consultation facilitation, represents a significant step toward democratizing healthcare access through technology while maintaining appropriate ethical boundaries and emphasizing the importance of professional medical care. Future work will focus on expanding the medical dataset to include more rare conditions and complex symptom patterns, supporting multilingual queries to serve diverse populations, and integrating voice-based interaction for improved accessibility and user experience. Additional development areas include real-time health monitoring integration with wearable devices, enhanced privacy protection measures to safeguard sensitive health information, incorporation of region-specific health trends to provide more relevant guidance, and optimization of user experience to create a more inclusive and robust healthcare assistance platform that serves the diverse needs of global healthcare consumers.

References

[1] Dr.Sandeep, Deep Learning-Based Crop Diseases Detection from leaf images, intrnational journal of enviornmenal sciences. [2] C. Crema et al., \"Medical Information Extraction with NLP-Powered QA Bots,\" Journal of Medical Systems, vol. 48, no. 3, pp. 1-15, 2024. [3] Athulya N et al., \"Healthcare Chatbot,\" International Journal of Engineering Research & Technology, vol. 10, no. 7, pp. 123-128, 2021. [4] M. Lawrence et al., \"Healthcare Chatbot System,\" in International Conference on Computer Science and Engineering, 2024, pp. 89-95. [5] M. V. Patil et al., \"AI-Based Healthcare Chatbot System,\" International Journal of Computer Applications, vol. 183, no. 25, pp. 12-18, 2021. [6] M. Jain et al., \"AI Healthcare Chatbot,\" in International Conference on Advanced Computing and Communication Systems, 2024, pp. 156-162. [7] P. Dohare et al., \"Good Fellow: A Healthcare Chatbot System,\" International Journal of Scientific Research in Computer Science Engineering and Information Technology, vol. 9, no. 4, pp. 78-85, 2023. [8] N. V. Shinde et al., \"Healthcare Chatbot System Using Artificial Intelligence,\" International Journal of Emerging Technologies and Innovative Research, vol. 8, no. 11, pp. 234-241, 2021. [9] R. Goel et al., \"Machine Learning-Based Healthcare Chatbot,\" in International Conference on Sustainable Computing and Data Communication Systems, 2022, pp. 445-451. [10] G. Chandel et al., \"Healthcare Chatbot for Personal Healthcare Assistance,\" International Journal of Advanced Research in Science, Communication and Technology, vol. 4, no. 2, pp. 167-174, 2024. [11] P. Kandpal, K. Jasnani, R. Raut, and S. Bhorge, \"Contextual chatbot for healthcare purposes (using deep learning),\" in 2020 Fourth World Conference on Smart Trends in Systems, Security and Sustainability (WorldS4), 2020, pp. 1-6. [12] R. K. Solanki, A. S. Rajawat, A. R. Gadekar, and M. E. Patil, \"Building a Conversational Chatbot Using Machine Learning: Towards a More Intelligent Healthcare Application,\" in Handbook of Research on Instructional Technologies in Health Education and Training, IGI Global, 2023, pp. 315-340. [13] S. Badlani, T. Aditya, M. Dave, and S. Chaudhari, \"Multilingual Healthcare Chatbot Using Machine Learning,\" in 2021 2nd International Conference for Emerging Technology (INCET), 2021, pp. 1-6.

Copyright

Copyright © 2025 Amith Y, Kavyashree G J. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.