Machine Learning Classification of Palatal Salivary Gland Tumors Using Clinical and Demographic Descriptors.
Objective: To investigate the potential of machine learning systems for the clinical classification of palatal salivary gland tumors into five diagnostic categories using only demographic and clinical data. Methods: Four machine learning models—Multilayer Perceptron (MLP), Support Vector Machine (SV...
| Publicado en: | Oral Diseases Vol. 32; no. 2; pp. 454 - 462 |
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| Autores principales: | , , , , , , , , , , , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
Feb2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=194012792&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 194012792 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 1354523X DZP jtl: Oral Diseases issn: 1354523X maglogo: Y pubinfo: dt: Feb2026 vid: 32 iid: 2 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 194012792 189357199 194012792 194012792 10.1111/odi.70104 194012792 ppf: 454 ppct: 8 formats: tig: atl: Machine Learning Classification of Palatal Salivary Gland Tumors Using Clinical and Demographic Descriptors. aug: au: de Melo Tassinari, Luís Arthur Araújo, Anna Luíza Damaceno de Sousa‐Neto, Sebastião Silvério Fonseca, Felipe Paiva Mariz, Bruno Augusto Linhares Almeida Martins, Manoela Domingues de Andrade, Bruno Augusto Benevenuto Agostini, Michelle Pontes, Hélder Antônio Rebelo Ribeiro, Ana Carolina Prado Brandão, Thaís Bianca Rocha, Andre Caroli Lopes, Marcio Ajudarte Vargas, Pablo Agustin Kowalski, Luiz Paulo Santos‐Silva, Alan Roger Moraes, Matheus Cardoso affil: Institute of Science and Technology (ICT‐UNIFESP), Federal University of São Paulo, São José Dos Campos, São Paulo, Brazil sug: subj: Machine Learning Algorithms Evaluation Classification Algorithms Evaluation Palatal Neoplasms Classification Salivary Gland Neoplasms Classification Palatal Neoplasms Diagnosis Salivary Gland Neoplasms Diagnosis Sociodemographic Factors Sensitivity and Specificity Evaluation Human Funding Source Multilayer Perceptrons Support Vector Machine Random Forest Boosting Machine Learning Algorithms Cancer Patients Validation Studies Palatal Neoplasms Symptoms Salivary Gland Neoplasms Symptoms Precision Prediction Models Retrospective Design Record Review Cross Sectional Studies Adenoma Myoepithelioma Carcinoma Adenocarcinoma Prospective Studies Descriptive Statistics Data Analysis Software ROC Curve Time Carcinoma, Adenoid Cystic Male Female Infant, Newborn Infant Child, Preschool Child Adolescence Adult Middle Age Aged Aged, 80 and Over Palatal Neoplasms Pathology Salivary Gland Neoplasms Pathology Histology Infant, Newborn: birth-1 month Infant: 1-23 months Child, Preschool: 2-5 years Child: 6-12 years Adolescent: 13-18 years Adult: 19-44 years Middle Aged: 45-64 years Aged: 65+ years Aged, 80 & over Male Female ab: Objective: To investigate the potential of machine learning systems for the clinical classification of palatal salivary gland tumors into five diagnostic categories using only demographic and clinical data. Methods: Four machine learning models—Multilayer Perceptron (MLP), Support Vector Machine (SVM), Random Forest (RF), and XGBoost—were implemented based on demographic and clinical attributes from 100 patients. The methodology involved a four‐step process: Hyperparameter optimization using systematic search for combinations (Grid Search), fivefold cross‐validation comprising training and testing, classifier training, and tests, followed by the acquisition of standardized performance metrics. The input attributes included age, sex, location, and symptoms. Performance was evaluated using accuracy, macro‐average sensitivity, specificity, precision, and F1‐score. Results: According to the mean accuracy values, XGBoost and MLP achieved the highest performance (81%), followed by SVM (80%) and RF (79%). Nevertheless, for all models, both macro‐averaged sensitivity and F1‐score were relatively low, remaining below 75%. Specificity emerged as the most consistent metric, ranging from 85% to 90% across all classifiers. All models reached a perfect score (1.0) in the classification of PA, whereas performance declined for malignant tumors, particularly for the rarer subtypes. Conclusions: Machine learning is a feasible approach for classifying palatal salivary gland tumors, demonstrating high specificity but limited sensitivity, primarily due to the uneven distribution of tumor subclasses, particularly the malignant ones, which are underrepresented owing to their rarity. XGBoost proved to be the most robust model with a low computational cost. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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