2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 17 , Issue 1 , PP: 01-15, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Surya A. 1 * , Chantilyan M. 2 , Chukka Ganesh 3 , Padmesh G. 4 , Patrick A. P. 5 , Raakesh G. 6 , S. Malathi 7
Doi: https://doi.org/10.54216/JISIoT.170101
The emergence of chatbots in the healthcare sector is increasingly pivotal, as they provide rapid and accessible assistance for the early detection of diseases and medical guidance. This study delineates a sophisticated two-tier healthcare chatbot system that synergistically integrates deep learning for image-based skin disease classification with machine learning for symptom-driven disease prediction. The system, developed in Python, employs a Hybrid U-Net & Improved MobileNet-V3 model to accurately identify dermatological conditions from images, while a Decision Tree Classifier is utilized to forecast diseases based on user-reported symptoms. Through meticulous evaluation of user inputs, the chatbot facilitates interactive consultations that encompass severity assessments, disease predictions, and preventive recommendations. Rigorous cross-validation of the symptom-based models, alongside testing on a bespoke dataset of skin disease images, substantiates the efficacy of the proposed methodology, demonstrating commendable predictive accuracy. The chatbot exemplifies significant potential by amalgamating conversational artificial intelligence with a hybrid approach of Hybrid U-Net & Improved MobileNet-V3 for image classification and Decision Tree Classifier for symptom analysis, thereby enhancing the landscape of telemedicine and patient care.
Healthcare chatbot , Decision Tree Classifier , Hybrid U-Net & , Improved MobileNet-V3 , Symptom analysis , Disease prediction , Artificial intelligence
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