Medibuddy- A Healthcare Chatbot using AI
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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
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Hussain, H., Aswani, K., Gupta, M. and Thampi, G.T., 2020. Implementation of disease prediction chatbot and report analyzer using the concepts of NLP, machine learning and OCR. Int. Res. J. Eng. Tech.(IRJET), 7, p.40.
L. Athota, V. K. Shukla, N. Pandey and A. Rana, "Chatbot for Healthcare System Using Artificial Intelligence," 2020 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), Noida, India, 2020, pp. 619-622, doi: 10.1109/ICRITO48877.2020.9197833 . https://doi.org/10.1109/ICRITO48877.2020.9197833
Kowatsch, T., Nißen, M., Shih, C.H.I., Rüegger, D., Volland, D., Filler, A., Künzler, F., Barata, F., Hung, S., Büchter, D. and Brogle, B., 2017. Text-based healthcare chatbots supporting patient and health professional teams: preliminary results of a randomized controlled trial on childhood obesity. Persuasive Embodied Agents for Behavior Change (PEACH2017).
Ni, L., Lu, C., Liu, N. and Liu, J., 2017, October. Mandy: Towards a smart primary care chatbot application. In International symposium on knowledge and systems sciences (pp. 38-52). Singapore: Springer Singapore. https://doi.org/10.1007/978-981-10-6989-5_4
Divya, S., Indumathi, V., Ishwarya, S., Priyasankari, M. and Devi, S.K., 2018. A self-diagnosis medical chatbot using artificial intelligence. Journal of Web Development and Web Designing, 3(1), pp.1-7.
Chung, K. and Park, R.C., 2019. Chatbot-based heathcare service with a knowledge base for cloud computing. Cluster Computing, 22, pp.1925-1937. https://doi.org/10.1007/s10586-018-2334-5
Akhil, S., 2016. An overview of tesseract OCR engine. In A seminar report. Department of Computer Science and Engineering National Institute of Technology, Calicut Monsoon.
Chaudhuri, A., Mandaviya, K., Badelia, P., K Ghosh, S., Chaudhuri, A., Mandaviya, K., Badelia, P. and Ghosh, S.K., 2017. Optical character recognition systems (pp. 9-41). Springer International Publishing. https://doi.org/10.1007/978-3-319-50252-6_2
Vinitha, S., Sweetlin, S., Vinusha, H. and Sajini, S., 2018. Disease prediction using machine learning over big data. Computer Science & Engineering: An International Journal (CSEIJ), 8(1), pp.1-8. https://doi.org/10.5121/cseij.2018.8101
Derrick, D.C., 2011. Special-Purpose, Embodied Conversational Intelligence with Environmental Sensors (SPECIES) agents: implemented in an automated interviewing kiosk. The University of Arizona. https://doi.org/10.17705/1thci.00027
Derrick, Douglas C., Jeffrey L. Jenkins, and Jay F. Nunamaker Jr. "Design principles for special purpose, embodied, conversational intelligence with environmental sensors (SPECIES) agents." AIS Transactions on Human-Computer Interaction 3, no. 2 (2011): 62-81. https://doi.org/10.17705/1thci.00027
Elkins, A.C., Derrick, D.C., Burgoon, J.K. and Nunamaker Jr, J.F., 2012, January. Predicting users' perceived trust in Embodied Conversational Agents using vocal dynamics.In 2012 45th Hawaii International Conference onSystemSciences (pp. 579-588). IEEE. https://doi.org/10.1109/HICSS.2012.483
Filler, A., Kowatsch, T., Haug, S., Wahle, F., Staake, T. andFleisch, E., 2015, April. MobileCoach: A novel opensourceplatform for the design of evidence-based, scalableandlow-cost behavioral health interventions: Overviewandpreliminary evaluation in the public health context. In2015wireless telecommunications symposium(WTS) (pp. 1-6).IEEE. https://doi.org/10.1109/WTS.2015.7117255
Bali, M., Mohanty, S., Chatterjee, Dr. S., Sarma, M., & Puravankara, R. (2019). Diabot: A Predictive Medical Chatbot using Ensemble Learning. In International Journal of Recent Technology and Engineering (IJRTE) (Vol. 8, Issue 2, pp. 6334–6340). https://doi.org/10.35940/ijrte.b2196.078219
Padmanabhan, Dr. M. (2019). Sustainable Test Path Generation for Chatbots using Customized Response. In International Journal of Engineering and Advanced Technology (Vol. 8, Issue 6, pp. 149–155). https://doi.org/10.35940/ijeat.d6515.088619
Arulmangainayaki, V., Harini, M., Keerthiga, M., & Priya, M. D. (2020). Intelligent Chatbot for Medical Assistance in Rural Areas. In International Journal of Innovative Technology and Exploring Engineering (Vol. 9, Issue 6, pp. 24–31). https://doi.org/10.35940/ijitee.f3083.049620