A new study published in Engineering has combined machine learning (ML) and experimental validation to identify dihydromyricetin (DHM), a natural flavonoid, as a potent inhibitor of the TGF-β/ALK5 ...

The study aimed to predict the risks of Major adverse cardiac events (MACE) in patients undergoing peritoneal dialysis (PD) with machine learning (ML) algorithm. In addition, we added the time factor ...

ABSTRACT: This paper proposes a hybrid machine learning framework for early diabetes prediction tailored to Sierra Leone, where locally representative datasets are scarce. The framework integrates ...

Diabetes is a chronic condition that affects a substantial portion of the global population and is linked to elevated mortality rates and a range of severe health complications. Despite its clinical ...

WEDNESDAY, Nov. 6, 2024 (HealthDay News) -- Clinical data and machine learning can help to predict intradialytic hypotension (IDH) for patients undergoing hemodialysis, according to a study published ...

Abstract: A commonly recognized chronic metabolic condition known as Diabetes mellitus significantly affects the global, social and economic standing of people. Obesity, age, high blood pressure, ...

A machine learning random forest regression system predicts a single numeric value. A random forest is an ensemble (collection) of simple decision tree regressors that have been trained on different ...

ABSTRACT: In order to reduce the risk of non-performing loans, losses, and improve the loan approval efficiency, it is necessary to establish an intelligent loan risk and approval prediction system. A ...

In the vast landscape of machine learning, Random Forests stand out as a formidable ensemble learning method. Their ability to handle complex datasets and produce robust predictions makes them a ...

In the field of data analysis and machine learning, collaboration and networking are crucial elements for success. During a recent research project, I faced challenges in selecting the best features ...