ItemConference Paper

AI-Based Medical Assistance forProactive Healthcare Predictions andServices

Title
AI-Based Medical Assistance forProactive Healthcare Predictions andServices
Creator
Sai, Vemula Harshith; John, Carolyn; Albin, S.; Nagaraju, Shamanth; Elappila, Manu; Athulya, S.
Description
Healthcare systems must adapt to the requirements of the digital era. The proposed healthcare Artificial Intelligence (AI) assistance provides a safe and user-friendly platform for physicians, patients, and administrators to meet their specific needs. The systems architecture prioritizes user authentication and role-based access control to ensure that only authorized users have access to certain features. The technology allows patients to input their symptoms, which is the platforms cornerstone offering. The technology uses a Machine Learning (ML) model and a large medical database to properly forecast probable illnesses based on the symptoms presented. This predictive feature helps individuals make educated decisions about their health and seek medical assistance proactively. The systems creative approach extends to online consultations. Patients may seek consultations, schedule appointments, and conduct secure video chats from the comfort of their homes. This online consultation service offers a convenient and flexible option for medical treatment, especially for people with restricted mobility or wanting immediate assistance. This paper evaluates disease prediction using parameters like accuracy and confusion matrix performance. The neural network model performs better for the above parameters in comparison to the random forest and K-nearest neighbor ML models. The proposed system uses ML technology to deliver fast, accurate, and secure medical services, breaking down traditional healthcare barriers. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
Source
Lecture Notes in Networks and Systems;Volume;1277 LNNS;pp.173-188
Date
01-01-2025
Publisher
Springer Science and Business Media Deutschland GmbH
Subject
AI; Chatbot; Confusion Matrix; Django; ML; Neural network
Coverage
Sai V.H., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Karnataka, Bengaluru, India; John C., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Karnataka, Bengaluru, India; Albin S., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Karnataka, Bengaluru, India; Nagaraju S., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Karnataka, Bengaluru, India; Elappila M., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Karnataka, Bengaluru, India; Athulya S., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Karnataka, Bengaluru, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISSN: 23673370; ISBN: 978-981962699-1;
Format
online
Language
English
Type
Conference paper

Citation

Sai, Vemula Harshith; John, Carolyn; Albin, S.; Nagaraju, Shamanth; Elappila, Manu; Athulya, S., “AI-Based Medical Assistance forProactive Healthcare Predictions andServices,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/25491.

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