Thiago Martini Pereira

Objective: This study aimed to develop and evaluate Machine Learning models to predict the malignant transformation (MT) in patients with actinic cheilitis (AC). Methods: Three hundred forty patients diagnosed with AC (322 in the no MT group, and 18 in the MT group) were carefully documented. The study used the Adaptive Synthetic Sampling to adaptively balance the dataset (322 in the no MT group and 319 in the MT group). Four supervised Machine Learning classifiers (Random Forest, Xtreme Gradient Boosting, Multilayer Perceptron, and Support Vector Machine) were trained and tested using 5‐fold cross‐validation to correlate inputs (clinical descriptors and demographic data) to outputs (MT). SHAP values were used to identify the most influential predictors of MT. Results: The Xtreme Gradient Boosting model stood out, achieving 96.72% accuracy, 96.87% sensitivity, 96.57% specificity, 96.61% precision, 96.73% of F1‐Score, and 0.9498 AUC. Multilayer Perceptron showed the best sensitivity (98.44%), and Random Forest presented comparable results. In contrast, Support Vector Machine underperformed, with higher values of false negatives and false positives. Across models, ulceration, multifocality, and long‐standing lesions were the strongest predictors of MT, while small, asymptomatic, or solitary lesions were associated with lower risk. Conclusion: 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.

This study evaluated class balancing strategies for classifying oral squamous cell carcinoma (OSCC) in FTIR hyperspectral images using the XGBoost model. Although the dataset was balanced at the image level, spectral quality filtering introduced pixel-level class imbalance. Resampling methods—SMOTE, Tomek Links, and their combination—were tested, as well as AllKNN for redundancy reduction. All approaches outperformed the unbalanced baseline, but the best overall performance metrics were achieved with the combined use of AllKNN and Tomek Links.

This study evaluates the classification of oral squamous cell carcinoma (OSCC) using micro-FTIR hyperspectral imaging and two tree-based models: Random Forest and XGBoost. After preprocessing and PCA, class imbalance was addressed with AllKNN and Tomek Links. Both models achieved high accuracy, and key spectral bands-Amide I, II, and III-were consistently highlighted, supporting their relevance in oral cancer detection.

Early detection and diagnosis of oral squamous cell carcinoma (OSCC) are essential for improving patient outcomes. This study presents the development of a deep neural network model trained to classify OSCC using hyperspectral FTIR spectroscopy imaging. The network's performance was evaluated using accuracy, precision, recall, and F1-score metrics. The model demonstrated high effectiveness in classifying unfolded OSCC hyperspectral images, showing potential as a valuable tool for supporting diagnostic processes in clinical settings.

Knowledge about the seasonal rainfall for some regions of Brazil is essential, due to the dependency of agriculture and for a correct management of water resources. For this, linear and nonlinear models are commonly used for seasonal rainfall prediction, some of them are based on Artificial Neural Networks, which have proved to have a great potential for this purpose. Following this idea, this work presents a seasonal rainfall forecast model based on a neuro-fuzzy technique, called Neo-Fuzzy Neuron Model, that showed a better performance, in terms of root mean square error and correlation coefficient between predicted and real output, when compared with a dynamic downscaling model using the Regional Spectral Model. The experimental results show the effectiveness of the proposed method in predicting the first four trimesters from 2002 up to the current year.