A machine learning lung cancer risk prediction model outperformed logistic regression, supporting improved risk assessment and more efficient radiology based lung cancer screening.

Background Annually, 4% of the global population undergoes non-cardiac surgery, with 30% of those patients having at least ...

Background The relationship of social determinants of health (SDOH), environmental exposures and medical history to lung function trajectories is underexplored. A better understanding of these ...

Introduction Application of artificial intelligence (AI) tools in the healthcare setting gains importance especially in the domain of disease diagnosis. Numerous studies have tried to explore AI in ...

Abstract: This study introduces a multi-model predictive control (MMPC) framework for quadrotor attitude regulation, combining the high-precision capabilities of nonlinear model predictive control ...

Implement Logistic Regression in Python from Scratch ! In this video, we will implement Logistic Regression in Python from Scratch. We will not use any build in models, but we will understand the code ...

Abstract: Credit card fraud detection presents a significant challenge due to the extreme class imbalance in transaction datasets. Traditional machine learning models struggle to achieve high recall ...

Objective: To identify risk factors for oral mucosal pressure injury (OMPI) in intensive care unit (ICU) patients undergoing orotracheal intubation and to develop and validate a risk prediction ...

Background: Post-stroke depression (PSD) is a prevalent neuropsychological consequence of stroke, associated with cognitive decline, disability, and increased mortality. Early prediction of PSD is ...