Models using established cardiovascular disease risk factors had satisfactory predictive performance for 5-year CVD risk in ...

In data analysis, time series forecasting relies on various machine learning algorithms, each with its own strengths. However, we will talk about two of the most used ones. Long Short-Term Memory ...

Abstract: Assumptions play a pivotal role in the selection and efficacy of statistical models, as unmet assumptions can lead to flawed conclusions and impact decision ...

Introduction Accurate preoperative assessment of lymph node metastasis (LNM) is a key determinant of treatment selection in early gastric cancer (EGC), particularly when choosing between endoscopic ...

Objective This study reviewed the current state of machine learning (ML) research for the prediction of sports-related injuries. It aimed to chart the various approaches used and assess their efficacy ...

Scientists are trying to tame the chaos of modern artificial intelligence by doing something very old fashioned: drawing a table. Instead of chemical elements, the new chart arranges learning ...

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

As climate extremes intensify across Africa, the need for accurate and timely weather prediction has become increasingly ...

A new research paper shows the approach performs significantly better than the random-walk forecasting method.