
International Journal For Multidisciplinary Research
E-ISSN: 2582-2160
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Impact Factor: 9.24
A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal
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Volume 7 Issue 3
May-June 2025
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Revolutionizing Digital Healthcare: An AI-Based Symptom Prediction and Treatment Advisory System
Author(s) | Ms. Bhumika Kishan Rawate, Ms. Mansi Dipak Marathe |
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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 |
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E-ISSN 2582-2160

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IJFMR DOI prefix is
10.36948/ijfmr
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