Predicting stunting status among under five children in ethiopia using ensemblemachine learning algorithms by Misganaw Ketema Ayele, Getachew Alemu Baye, Seid Hassen Yesuf, Abebaw Agegne Engda, Eshetie Teka Mitiku

“…This study overcame a key limitation in previous stunting prediction models by developing a multi-class classification model that predicts stunting severity (severe, moderate, normal) using Ethiopia’s nationally representative EDHS data from 2011 to 2016. …”
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Construction of a prediction model for moderate to severe perimenopausal syndrome based on machine learning algorithms by ZHANG Min, GU Tingting, GUAN Wei, LIU Xiangxiang, SHI Junyao

“…Objective To identify risk factors for perimenopausal syndrome (PMS) among perimenopausal women using machine learning algorithms, and to construct a predictive model for the risk of developing moderate to severe PMS in perimenopausal women. …”
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Machine Learning Applications for Predicting High-Cost Claims Using Insurance Data by Esmeralda Brati, Alma Braimllari, Ardit Gjeçi

“…This study aimed to empirically evaluate the performance of classification algorithms, including Logistic Regression, Decision Tree, Random Forest, XGBoost, K-Nearest Neighbors, Support Vector Machine, and Naïve Bayes to predict high insurance claims. …”
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Exploring machine learning algorithms for predicting fertility preferences among reproductive age women in Nigeria by Zinabu Bekele Tadese, Teshome Demis Nimani, Kusse Urmale Mare, Fetlework Gubena, Ismail Garba Wali, Jamilu Sani

“…Six machine learning algorithms, namely, Logistic Regression, Support Vector Machine, K-Nearest Neighbors, Decision Tree, Random Forest, and eXtreme Gradient Boosting, were employed on a total sample size of 37,581 in Python 3.9 version. …”
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Comparison of Machine Learning Algorithms to Predict Down Syndrome During the Screening of the First Trimester of Pregnancy by Eduardo Alonso, Andoni Beristain, Jorge Burgos, Ibai Gurrutxaga

“…The trained classification algorithms achieved ROC-AUC values between 0.970 and 0.982, with sensitivity and specificity of 0.94. …”
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Machine learning algorithms to predict stroke in China based on causal inference of time series analysis by Qizhi Zheng, Ayang Zhao, Xinzhu Wang, Yanhong Bai, Zikun Wang, Xiuying Wang, Xianzhang Zeng, Guanghui Dong

“…Conclusions and Relevance This study proposes a stroke risk prediction method that combines dynamic causal inference with machine learning models, significantly improving prediction accuracy and revealing key health factors that affect stroke. …”
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Predicting and controlling the reactivity of immune cell populations against cancer by Kfir Oved, Eran Eden, Martin Akerman, Roy Noy, Ron Wolchinsky, Orit Izhaki, Ester Schallmach, Adva Kubi, Naama Zabari, Jacob Schachter, Uri Alon, Yael Mandel‐Gutfreund, Michal J Besser, Yoram Reiter

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Research on civil aircraft cockpit display interface availability considering multidimensional indicators clustering and reduction by CHEN Dengkai, XIAO Yao, XIAO Jianghao, ZHOU Yao, YANG Cong

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Prediction Model of Phosphorus Content at the End Point of Electroslag Remelting Based on MI and XGBoost Algorithms by Liu　Yuxiao, Dong　Yanwu, Jiang　Zhouhua, Chen　Xi

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Prediction of Body Mass of Dairy Cattle Using Machine Learning Algorithms Applied to Morphological Characteristics by Franck Morais de Oliveira, Patrícia Ferreira Ponciano Ferraz, Gabriel Araújo e Silva Ferraz, Marcos Neves Pereira, Matteo Barbari, Giuseppe Rossi

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A Comprehensive Review of AI Algorithms for Performance Prediction, Optimization, and Process Control in Desalination Systems by Mahmoud Ibnouf, Hadi Jaber, Hadil Abukhalifeh, Mohammed Ghazal, Mohamad Ramadan, Mohammad Alkhedher

“…This comprehensive review examines the various AI algorithms employed in desalination literature. In addition, it reviews their various applications which include performance prediction models. …”
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Solar energy prediction through machine learning models: A comparative analysis of regressor algorithms. by Huu Nam Nguyen, Quoc Thanh Tran, Canh Tung Ngo, Duc Dam Nguyen, Van Quan Tran

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The Use of Machine Learning Algorithms for Water Quality Index Prediction in the Sai Gon River, Vietnam by Thuy Nguyen Thi Diem, Mai Nguyen Thi Huynh, Tra Tran Quang

“…The present study leverages the predictive performance of several ML algorithms, including extreme gradient boosting (XGB), the gradient boosting model (GBM), support vector regression (SVR), and the radial basic function (RBF), to predict the WQI at three monitoring sites on the Sai Gon River from 2015–2019. …”
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Biomarker identification and gene-drug interaction prediction for breast cancer using machine learning algorithms by Raja Abhavya, Pragya Pragya, Sabitha R., Kumar Brijesh, Agastinose Ronickom Jac Fredo

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Predicting neonatal mortality using ensemble machine learning algorithms in the case of Ethiopian Rural Areas by Melaku Alelign Mengstie, Misganaw Telake Telele

“…Additionally, the logistic regression algorithm was employed to enhance transparency and interpretability and for comparative analysis. …”
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Enhancing Support Vector Classification for Diabetes Prediction with Novel Optimization Algorithms of Intelligent Health Services by Debojani Paul Chowdhury, Aditi Paul Chowdhury, Apurba Das, Pinki Pinki

“…Additionally, 3 novel metaheuristic algorithms, Quadratic Interpolation Optimizer (QIO), Tunicate Swarm Algorithm (TSA), and African Vulture Optimization Algorithm (AVOA) were utilized to enhance the SVC’s performance. …”
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Replacing Gauges with Algorithms: Predicting Bottomhole Pressure in Hydraulic Fracturing Using Advanced Machine Learning by Samuel Nashed, Rouzbeh Moghanloo

“…The primary objective of this study is to produce sophisticated machine learning algorithms that can accurately predict bottomhole pressure while injecting guar cross-linked fluids into the fracture string. …”
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A Novel Model for Accurate Daily Urban Gas Load Prediction Using Genetic Algorithms by Xi Chen, Feng Wang, Li Xu, Taiwu Xia, Minhao Wang, Gangping Chen, Longyu Chen, Jun Zhou

“…A multiple weather parameter–daily load prediction (MWP-DLP) model based on System Thermal Days (STD) was established, and the genetic algorithm was used to solve the model. …”
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Application of bioinspired global optimization algorithms to the improvement of the prediction accuracy of compact extreme learning machines by L. A. Demidova, A. V. Gorchakov

“…The obtained results showed that the prediction accuracy of ELMs can be improved by using bioinspired algorithms for the intelligent adjustment of input weights. …”
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Optimized machine learning algorithms with SHAP analysis for predicting compressive strength in high-performance concrete by Samuel Olaoluwa Abioye, Yusuf Olawale Babatunde, Oluwafikejimi Abigail Abikoye, Aisha Nene Shaibu, Bailey Jonathan Bankole

“…Abstract This research examines the application of eight different machine learning (ML) algorithms for predicting the compressive strength of high-performance concrete (HPC). …”
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