Comparative analysis of dimensionality reduction techniques for EEG-based emotional state classification.

Objectives: The aim of this study is to evaluate the impact of various dimensionality reduction methods, including principal component analysis (PCA), Laplacian score, and Chi-square feature selection, on the classification performance of an electroencephalogram (EEG) dataset.

Methods: We applied dimensionality reduction techniques, including PCA, Laplacian score, and Chi-square feature selection, and assessed their impact on the classification performance of EEG data using linear regression, K-nearest neighbour (KNN), and Naive Bayes classifiers. The models were evaluated in terms of their classification accuracy and computational efficiency.

Advancing prognostic precision in pulmonary embolism: A clinical and laboratory-based artificial intelligence approach for enhanced early mortality risk stratification.

Background: Acute pulmonary embolism (PE) is a critical medical emergency that necessitates prompt identification and intervention. Accurate prognostication of early mortality is vital for recognizing patients at elevated risk for unfavourable outcomes and administering suitable therapy. Machine learning (ML) algorithms hold promise for enhancing the precision of early mortality prediction in PE patients.