DiagnoBot: A Medical Chatbot
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Date
2024-01-03
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5216
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Abstract
Many 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.
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Design and Architectures of Data-Centric and Knowledge Based Systems, chatbot, disease prediction., machine learning
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9 pages
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Proceedings of the 57th Hawaii International Conference on System Sciences
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Attribution-NonCommercial-NoDerivatives 4.0 International
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