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 study offers an in-depth comparative evaluation of various machine learning methods for malware detection using Windows Portable Executable (PE) files. We evaluate three distinct classifiers: ...

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 ...

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 ...

1 Department of Information Technology and Computer Science, School of Computing and Mathematics, The Cooperative University of Kenya, Nairobi, Kenya. 2 Department of Computing and Informatics, School ...

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

Abstract: This study presents a data-driven framework using machine learning algorithms to address three key aspects of diabetes management: diagnosis, hospital readmission prediction, and healthcare ...

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 ...

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Random forest machine learning regression and interactive web application using home loan data to help determine algorithmic discrimination toward race and gender in ...