Association between parities and duration of breastfeeding and the severity of coronary artery disease in women above 30 years old age (A pilot study).

Background: The prevalence and mortality of CVD in women increase over time. We conducted this research to evaluate the severity of coronary artery disease with the number of live births and breastfeeding duration.

Methods: Patients aged 30-50 years old with positive exercise tests or evidence of cardiac ischemia who were candidates for coronary angiography were included.

Artificial intelligence approaches for tinnitus diagnosis: leveraging high-frequency audiometry data for enhanced clinical predictions.

This research investigates the application of machine learning to improve the diagnosis of tinnitus using high-frequency audiometry data. A Logistic Regression (LR) model was developed alongside an Artificial Neural Network (ANN) and various baseline classifiers to identify the most effective approach for classifying tinnitus presence. The methodology encompassed data preprocessing, feature extraction focused on point detection, and rigorous model evaluation through performance metrics including accuracy, Area Under the ROC Curve (AUC), precision, recall, and F1 scores.

Investigating the effect of hot and cold polyurethane foam on reducing whole body vibration of forklift operators.

Background: Vibration is one of the harmful factors for forklift drivers. The use of non- standard seats and not paying attention to how the seats are maintained can be affected by the amount of vibration transmitted to the person.

Objective: This study investigates the amount of vibration transmitted from the forklift and the effect of different types of polyurethane foam in reducing the vibration transmitted from the forklift seat.

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.