DiagnoBot: A Medical Chatbot

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dc.contributor.authorNixon, Cheyenne
dc.contributor.authorO’Barr, Benjamin
dc.contributor.authorGu, Keugmo
dc.date.accessioned2023-12-26T18:46:08Z
dc.date.available2023-12-26T18:46:08Z
dc.date.issued2024-01-03
dc.identifier.doihttps://doi.org/10.24251/HICSS.2024.626
dc.identifier.isbn978-0-9981331-7-1
dc.identifier.other4cbc4c87-a7a2-4e2d-81c5-b6b8b16d961c
dc.identifier.urihttps://hdl.handle.net/10125/107011
dc.language.isoeng
dc.relation.ispartofProceedings of the 57th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectDesign and Architectures of Data-Centric and Knowledge Based Systems
dc.subjectchatbot
dc.subjectdisease prediction.
dc.subjectmachine learning
dc.titleDiagnoBot: A Medical Chatbot
dc.typeConference Paper
dc.type.dcmiText
dcterms.abstractMany people live without access to healthcare or delay care due to inconvenience, work, cost, living in rural areas, or social/medical fears (Gertz, Pollack, Schultheiss, & Brownstein, 2022), (Golembiewski, et al., 2022). Medical chatbots have emanated as a potential solution to healthcare access and to promote self-care. Our goal is to provide medical information through conversation to those who may otherwise delay seeking care. A Rasa chatbot is created using our Disease Prediction System, which utilizes machine learning algorithms i.e., Decision Trees, Gradient Boosting, Support Vector Machine (SVM), and Naïve Bayes to guide users to a sensible diagnosis, so they may opt to self-care at home or seek medical attention. In this paper, a sample of 4920 patient records with 41 disorders is analyzed. A Recursive Feature Elimination algorithm is used to enhance 95 out of the 132 symptom features. Our system achieved 97-100 percent accuracy.
dcterms.extent9 pages
prism.startingpage5216

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Design and Architectures of Data-Centric and Knowledge Based Systems