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International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
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Issue: February 2026
Volume 11 | Issue 2
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Impact Factor : 8.14
Issue per Year : 12
Volume Published : 11
Issue Published : 117
Article Submitted : 21318
Article Published : 8476
Total Authors : 22301
Total Reviewer : 802
Total Countries : 156
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
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Published Paper Details
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| Paper Title: | A Patient-Centric Explainable AI System for Tracking Longitudinal Breast Cancer Risk Trends |
| Authors Name: | harshwardhan sahu , Dr. PARTAP SINGH |
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| Author Reg. ID: |
IJRTI_208167
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| Published Paper Id: | IJRTI2512141 |
| Published In: | Volume 10 Issue 12, December-2025 |
| DOI: | |
| Abstract: | Breast cancer is one of the most prevalent malignancies affecting women globally, and early detection remains critical in ensuring positive health outcomes. Modern artificial intelligence (AI) systems have achieved significant accuracy in breast cancer prediction using structured diagnostic features; however, they often fail to address two clinical necessities: explainability and longitudinal risk tracking. Clinicians require transparent reasoning behind AI decisions and the ability to monitor a patient’s risk progression across multiple diagnostic visits. This paper proposes a patient-centric Explainable AI (XAI) system designed to assess, interpret, and track breast cancer risk over time. By integrating machine learning models, SHAP and LIME explainability methods, OCR-based report extraction, and longitudinal trend analytics, the system enhances clinical decision-making, improves trust in AI-assisted diagnosis, and supports patient follow-up planning. Experimental results demonstrate that explainable risk trajectories provide actionable insights into disease progression, enabling early clinical intervention. |
| Keywords: | Breast cancer, Explainable AI, SHAP, LIME, longitudinal analysis, medical diagnosis, machine learning, risk tracking, patient-centric AI. |
| Cite Article: | " A Patient-Centric Explainable AI System for Tracking Longitudinal Breast Cancer Risk Trends ", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 12, page no.b324-b337, December-2025, Available :http://www.ijrti.org/papers/IJRTI2512141.pdf |
| Downloads: | 000147 |
| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2512141
Registration ID:208167
Published In: Volume 10 Issue 12, December-2025
DOI (Digital Object Identifier):
Page No: b324-b337 Country: Chhindwara, Madhya Pradesh, India Research Area: Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2512141 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2512141 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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