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Oyebayo Ridwan Olaniran on Muck Rack

Oyebayo Ridwan Olaniran

As seen in: MDPI
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Articles

Random Generalized Additive Logistic Forest: A Novel Ensemble Method for Robust Binary Classification

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By Oyebayo Ridwan Olaniran, Asma Alzahrani, Oyebayo Ridwan, Ali Rashash R
| MDPI
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.
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Bayesian Random Forest with Multiple Imputation by Chain Equations for High-Dimensional Missing Data: A Simulation Study

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By Oyebayo Ridwan Olaniran, Oyebayo Ridwan, Ali Rashash R
| MDPI
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.
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Hybrid Random Feature Selection and Recurrent Neural Network for Diabetes Prediction

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By Oyebayo Ridwan Olaniran, Jeza Allohibi, Abdulmajeed Atiah Alharbi, Aliu Omotayo Sikiru
| MDPI
1. Introduction The widespread adoption of machine learning (ML) in prediction tasks has yet to fully address the growing global burden of chronic diseases such as diabetes, which is projected to affect 700 million people by 2045 [1]. Diabetes, categorized into type 1 (T1D) and type 2 (T2D), poses significant health risks to children and adults, respectively [1,2].
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