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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Impact Factor : 8.14
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| Paper Title: | AI-Powered ThyroScanner: An Ensemble Learning-Based System for Accurate Thyroid Disease Classification |
| Authors Name: | Mr S Dhanasekar , E Venkateswaran , S Manojkumar , M Nagaraj , K Prabhu |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_202319
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| Published Paper Id: | IJRTI2504150 |
| Published In: | Volume 10 Issue 4, April-2025 |
| DOI: | http://doi.one/10.1729/Journal.44866 |
| Abstract: | Thyroid diseases are one of the most common endocrine illnesses, impacting millions of people globally. Conventional diagnosis is based on visual interpretation of thyroid function tests (TFTs), which is time-consuming, subjective, and susceptible to human error, resulting in misdiagnosis and delayed treatment. To overcome these limitations, we introduce ThyroScanner, a sophisticated Computer-Aided Diagnosis (CAD) system using ensemble machine learning for accurate thyroid disease classification. ThyroScanner applies the XGBoost algorithm with feature selection (Chi-square test) and feature extraction (co-occurrence matrix) to categorize thyroid conditions into three: no thyroid disease, hypothyroidism, and hyperthyroidism. The system evaluates clinical parameters such as Thyroid-Stimulating Hormone (TSH), Free Thyroxine (FT4), Triiodothyronine (T3), age, and gender to provide objective and precise diagnoses. Large-scale validation proves ThyroScanner to be 94.5% accurate, 93.8% precise, and 92.6% sensitive, outdoing conventional diagnostic procedures and other machine learning models. The system is scalable, efficient, and can be implemented in clinical environments, minimizing diagnostic latency and enhancing patient outcomes. With automated feature engineering and ensemble learning, ThyroScanner creates a new standard in thyroid disease prediction, providing a solid, trustworthy, and real-time diagnostic platform for clinicians. |
| Keywords: | ThyroScanner , Thyroid Disease, Classification, AI , Ensemble Learning System |
| Cite Article: | "AI-Powered ThyroScanner: An Ensemble Learning-Based System for Accurate Thyroid Disease Classification", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 4, page no.b417-b423, April-2025, Available :http://www.ijrti.org/papers/IJRTI2504150.pdf |
| Downloads: | 000368 |
| 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: IJRTI2504150
Registration ID:202319
Published In: Volume 10 Issue 4, April-2025
DOI (Digital Object Identifier): http://doi.one/10.1729/Journal.44866
Page No: b417-b423 Country: Tiruppur , Tamilnadu , India Research Area: Computer Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2504150 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2504150 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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