Machine Learning for Predicting Malignant Transformation in Actinic Cheilitis: A Prognostic Support System Based on Demographic and Clinical Descriptors
The results revealed promising performance metrics for Xtreme Gradient Boosting and Multilayer Perceptron suggesting their potential value as tools in a support system for monitoring AC. Additionally, synthetic data proved constructive in training, enhancing the models' robustness and predictive capabilities.
Authors
- Raísa Sales de Sá (ORCID: https://orcid.org/0000-0003-4563-6531)
- Ivan José Correia‐Neto
- Cristina Saldivia‐Siracusa (ORCID: https://orcid.org/0000-0002-2422-2164)
- Anna Luíza Damaceno Araújo (ORCID: https://orcid.org/0000-0002-3725-8051)
- Matheus Cardoso Moraes (ORCID: https://orcid.org/0000-0002-6019-6653)
- Alan Roger Santos‐Silva
- Thiago Martini Pereira
- Luiz Paulo Kowalski
- Marcio Ajudarte Lopes
- Pablo Agustin Vargas
- Alex Franco da Costa
Institutions
- Universidade de São Paulo (BR)
- Universidade Estadual de Campinas (UNICAMP) (BR)
- Hospital Israelita Albert Einstein (BR)
- Universidade Ceuma (BR)
- Universidade Federal de São Paulo (BR)
Publication Details
- Journal
- Journal of Oral Pathology and Medicine
- Published
- 2026-01-19
- DOI
- https://doi.org/10.1111/jop.70113
- Citations
- 1
- Primary Topic
- Autoimmune and Inflammatory Disorders
- Type
- article
- Field-Weighted Citation Impact
- 31.52