Prostate Cancer Classification Using Multimodal Feature Fusion and Explainable AI

Prostate cancer, the second most prevalent male malignancy, requires advanced diagnostic tools. We propose an explainable AI system combining BERT (for textual clinical notes) and Random Forest (for numerical lab data) through a novel multimodal fusion strategy, achieving superior classification performance on PLCO-NIH dataset (98% accuracy, 99% AUC). While multimodal fusion is established, our work demonstrates that a simple yet interpretable BERT+RF pipeline delivers clinically significant improvements - particularly for intermediate cancer stages (Class 2/3 recall: 0.900 combined vs 0.824 numerical/0.725 textual). SHAP analysis provides transparent feature importance rankings, while ablation studies prove textual features' complementary value. This accessible approach offers hospitals a balance of high performance (F1=89%), computational efficiency, and clinical interpretability - addressing critical needs in prostate cancer diagnostics.

Brain2Vec: A Deep Learning Framework for EEG-Based Stress Detection Using CNN-LSTM-Attention

Mental stress has become a pervasive factor affecting cognitive health and overall well-being, necessitating the development of robust, non-invasive diagnostic tools. Electroencephalogram (EEG) signals provide a direct window into neural activity, yet their non-stationary and high-dimensional nature poses significant modeling challenges. Here we introduce Brain2Vec, a new deep learning tool that classifies stress states from raw EEG recordings using a hybrid architecture of convolutional, recurrent, and attention mechanisms. The model begins with a series of convolutional layers to capture localized spatial dependencies, followed by an LSTM layer to model sequential temporal patterns, and concludes with an attention mechanism to emphasize informative temporal regions. We evaluate Brain2Vec on the DEAP dataset, applying bandpass filtering, z-score normalization, and epoch segmentation as part of a comprehensive preprocessing pipeline. Compared to traditional CNN-LSTM baselines, our proposed model achieves an AUC score of 0.68 and a validation accuracy of 81.25%. These findings demonstrate Brain2Vec's potential for integration into wearable stress monitoring platforms and personalized healthcare systems.