Location
Hilton Hawaiian Village, Honolulu, Hawaii
Event Website
https://hicss.hawaii.edu/
Start Date
3-1-2024 12:00 AM
End Date
6-1-2024 12:00 AM
Description
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.
Recommended Citation
Nixon, Cheyenne; O’Barr, Benjamin; and Gu, Keugmo, "DiagnoBot: A Medical Chatbot" (2024). Hawaii International Conference on System Sciences 2024 (HICSS-57). 3.
https://aisel.aisnet.org/hicss-57/ks/architecture/3
DiagnoBot: A Medical Chatbot
Hilton Hawaiian Village, Honolulu, Hawaii
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.
https://aisel.aisnet.org/hicss-57/ks/architecture/3