Improving thyroid disorder diagnosis via innovative stacking ensemble learning model
These results reveal the capability of ML, especially ensemble approaches, to enhance accurate and timely diagnosis of thyroid disorders.
Authors
- Muhammad Syafrudin (ORCID: https://orcid.org/0000-0002-5640-4413)
- Ayesha Hassan (ORCID: https://orcid.org/0009-0007-8788-3617)
- Norma Latif Fitriyani (ORCID: https://orcid.org/0000-0002-1133-3965)
- Muhammad Munwar Iqbal (ORCID: https://orcid.org/0000-0001-7212-1408)
- Shabana Ramzan (ORCID: https://orcid.org/0000-0001-9418-6667)
- Ali Raza (ORCID: https://orcid.org/0000-0001-5429-9835)
- Seung Won Lee (ORCID: https://orcid.org/0000-0001-5632-5208)
- Aseel Smerat (ORCID: https://orcid.org/0009-0008-4600-509X)
Institutions
- Al-Ahliyya Amman University (JO)
- University of Lahore (PK)
- Mustansiriyah University (IQ)
- University of Engineering and Technology Taxila (PK)
- Government Sadiq College Women University (PK)
- Sejong University (KR)
- Chitkara University (IN)
- Sungkyunkwan University (KR)
Publication Details
- Journal
- Digital Health
- Published
- 2025-05-01
- DOI
- https://doi.org/10.1177/20552076251341430
- Citations
- 6
- Primary Topic
- Artificial Intelligence in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 17.63
Funders
- National Research Foundation of Korea