The rapid uptake of supervised machine learning (ML) in clinical prediction modelling, particularly for binary outcomes based on tabular data, has sparked debate about its comparative advantage over ...

Random forest regression is a tree-based machine learning technique to predict a single numeric value. A random forest is a collection (ensemble) of simple regression decision trees that are trained ...

Random forest regression is a tree-based machine learning technique to predict a single numeric value. A random forest is a collection (ensemble) of simple regression decision trees that are trained ...

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 Random Forest ...

Acute ischemic stroke (AIS) patients often experience poor functional outcomes post-intravenous thrombolysis (IVT). Novel computational methods leveraging machine learning (ML) architectures ...

Abstract: This paper describes a machine learning-based analysis of crop yield prediction across the states of India using a Logistic Regression and Random Forest classifiers. The analysis relied on ...

Abstract: This study aims to evaluate and compare the performance of five machine learning algorithms, namely Bayesian Additive Regression Trees (BART), Bayesian Geoadditive Regression Trees (BART ...

Stroke remains one of the leading causes of global mortality and long-term disability, driving the urgent need for accurate and early risk prediction tools. Traditional models such as the Framingham ...

ABSTRACT: The COVID-19 pandemic exposed critical vulnerabilities in global medical supply chains, resulting in widespread shortages of essential healthcare products. This study aims to optimize the ...