International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 7, Issue 3 (May-June 2025) Submit your research before last 3 days of June to publish your research paper in the issue of May-June.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 7 Issue 3
May-June 2025

Submit your research paper

Indexing Partners
Academia Advanced Sciences Index Bielefeld Academic Search Engine CiteSeer DRJI Google Scholar Independent Search Engine & Directory Network ISI (International Scientific Indexing) Issuu Mendeley Research Networks
RefSeek ResearcherId - Thomson Reuters ResearchGate Scirus Scribd Semantic Scholar UTeM - Universiti Teknikal Malaysia Melaka Wiki for Call for Papers WorldCat Zenodo

Revolutionizing Digital Healthcare: An AI-Based Symptom Prediction and Treatment Advisory System

Author(s) Ms. Bhumika Kishan Rawate, Ms. Mansi Dipak Marathe
Country India
Abstract This study explores development and implementation of a personalized, AI-driven health advisory system designed to support individuals in identifying potential illnesses based on self-reported symptoms. One of the major challenges in early-stage diagnosis is the lack of accessible, reliable, and real-time tools that can guide users before they seek professional care. The system integrates a machine learning-based symptom checker with interactive healthcare features, delivering real-time disease predictions along with detailed information, medication suggestions, preventive measures, and lifestyle guidance. By combining health prediction technology with user-friendly interface design through Streamlit, the platform enhances engagement and ease of use. This initiative presents a multi-functional health support system that aims to empower users with essential knowledge and promote preventive healthcare practices. The AI model was trained on medical datasets and demonstrated a disease prediction accuracy of over 95 percent, enabling it to suggest relevant outcomes with high reliability. Experimental results highlight the model’s capability to provide accurate guidance, making it a supportive tool in both self-care and early intervention.
Keywords AI in Digital healthcare , Machine Learning Operations , AI-Powered Health Assistant , Medical Recommendations , Predictive Analytics
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 7, Issue 3, May-June 2025
Published On 2025-05-04
DOI https://doi.org/10.36948/ijfmr.2025.v07i03.42191
Short DOI https://doi.org/g9hscv
View / Download PDF File

Share this



E-ISSN 2582-2160
CrossRef

CrossRef DOI is assigned to each research paper published in our journal.

IJFMR DOI prefix is
10.36948/ijfmr

Downloads
Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 7 Isu 2Cover Page Vol 7 Isu 1Cover Page Vol 6 Isu 6

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA Open Access

About IJFMR
Fees & Payment
Current Issue
Publication Archive 		Submit Research Paper
Track Submission Status
Publication Guidelines
Publication Ethics
Peer Review & Plagiarism 		Join as a Reviewer
Editors & Reviewers
Reviewer Referral Program
Get Reviewer Membership Certi. 		Website/Journal Policies
Usage Policy
Content Policies
Privacy Policy
Contact Us +91-9687-828-838 editor@ijfmr.com