Medibuddy- A Healthcare Chatbot using AI
Ruchita Singhania1, Sana Badagan2, Deeksha Reddy3, K Tarun Sai Teja4, Chetan Jett5
1Ruchita Singhania, Department of AIML, Dayananda Sagar Academy of Technology and Management, Bangalore (Karnataka), India.
2Sana Badagan, Department of AIML, Dayananda Sagar Academy of Technology and Management, Bangalore (Karnataka), India.
3Deeksha Reddy, Department of AIML, Dayananda Sagar Academy of Technology and Management, Bangalore (Karnataka), India.
4K Tarun Sai Teja, Department of AIML, Dayananda Sagar Academy of Technology and Management, Bangalore (Karnataka), India.
5Chetan Jetty, Department of AIML, Dayananda Sagar Academy of Technology and Management, Bangalore (Karnataka), India.
Manuscript received on 23 May 2024 | Revised Manuscript received on 29 June 2024 | Manuscript Accepted on 15 July 2024 | Manuscript published on 30 July 2024 | PP: 14-19 | Volume-14 Issue-3, July 2024 | Retrieval Number: 100.1/ijsce.G990213070624 | DOI: 10.35940/ijsce.G9902.14030724
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This paper presents the development of a Flask-based web application designed to predict diseases based on user-reported symptoms and provide relevant health information. Leveraging machine learning techniques, the system utilizes a dataset of diseases and their associated symptoms to generate predictions through cosine similarity and a pre-trained Random Forest model. The application features a user-friendly interface for registration, login, and symptom reporting. Additionally, it integrates the DuckDuckGo search API to fetch detailed information about predicted diseases, enhancing the user experience with comprehensive health insights. The application also includes an interactive chatbot to guide users through the symptom input process, ensuring accurate data collection for reliable disease prediction. The system is built with Python, utilizing libraries such as pandas, numpy, and scikit-learn for data processing and model deployment, and is powered by SQLAlchemy for database management. This work aims to provide an accessible tool for preliminary health assessment, potentially aiding in early diagnosis and prompt medical.
Keywords: Random Forest Model, DuckDuckGo API, Health info, Cosine Similarity.
Scope of the Article: Computer Science and Its Applications