Healthcare Chatbot Using AI

Abstract - In today's era, India faces a major challenge in providing affordable and accessible healthcare, especially in rural areas where transport and quality facilities are limited. To address this, we developed an AI-powered Healthcare Chatbot using Python. This system helps users get instant responses to health-related queries and locate nearby doctors, clinics, and hospitals using the Google Places API—crucial during emergencies. The project has two modules: User and Admin. Users can register, log in, manage profiles, chat with the bot, and access healthcare facility information. Admins can manage the Q&A dataset, train the model, and monitor user activity. We built a custom dataset and used a CNN algorithm for improved disease risk prediction accuracy. Key Words: Facial recognition, LBPH, Haar Cascade, Attendance System, Computer Vision, Face Detection

Authors

Publication Details

Journal
INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT
Published
2025-06-11
DOI
https://doi.org/10.55041/ijsrem49884
Primary Topic
AI in Service Interactions
Type
article
Field-Weighted Citation Impact
0.00
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article

Healthcare Chatbot Using AI

Kobbaji Saiteja
INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT
AI in Service Interactions
article

Healthcare Chatbot Using AI

Kobbaji Saiteja
article en

Abstract

Abstract - In today's era, India faces a major challenge in providing affordable and accessible healthcare, especially in rural areas where transport and quality facilities are limited. To address this, we developed an AI-powered Healthcare Chatbot using Python. This system helps users get instant responses to health-related queries and locate nearby doctors, clinics, and hospitals using the Google Places API—crucial during emergencies. The project has two modules: User and Admin. Users can register, log in, manage profiles, chat with the bot, and access healthcare facility information. Admins can manage the Q&A dataset, train the model, and monitor user activity. We built a custom dataset and used a CNN algorithm for improved disease risk prediction accuracy. Key Words: Facial recognition, LBPH, Haar Cascade, Attendance System, Computer Vision, Face Detection

INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENTVol. 09(06)
Openalex Percentile: Top 94%
AI in Service Interactions
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