An algorithm to assess importance of predictors in systematic reviews of prediction models: a case study with simulations by Ruohua Yan, Chen Wang, Chao Zhang, Xiaohang Liu, Dong Zhang, Xiaoxia Peng

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Prediction of Telkomsel 4G LTE Card Sales using The K-Nearest Neighbor Algorithm by Alfiana Fontes Martins, Yasinta Oktaviana Legu Rema, Debora Chrisinta, Alejandro Jr. V. Matute, Krisantus Jumarto Tey Seran

“…This study aims to develop a precise model for predicting card sales using the K-Nearest Neighbor (KNN) algorithm and to offer recommendations for improving prediction quality by addressing issues related to data imbalance and overfitting. …”
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Optimization of machine learning algorithms for proteomic analysis using topsis by Javanbakht T., Chakravorty S.

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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

“…Methods This study aimed to develop a predictive model for neonatal mortality in rural Ethiopia using secondary data from the Ethiopian Demographic and Health Surveys (2000–2019). …”
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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

“…This analytical skill enables practitioners to extract meaningful insights from the data, which supports well-informed decision-making and accurate result prediction. Support Vector Classification (SVC) was employed to predict Diabetes in this study. …”
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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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Landslide spatial prediction using data-driven based statistical and hybrid computational intelligence algorithms by Xia Zhao, Wei Chen, Paraskevas Tsangaratos, Ioanna Ilia, Qingfeng He

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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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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

“…Hence, this study aimed to predict the fertility preferences of reproductive age women in Nigeria using state-of-the-art machine learning techniques.MethodsSecondary data analysis from the recent 2018 Nigeria Demographic and Health Survey dataset was employed using feature selection to identify predictors to build machine learning models. …”
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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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Prediction of Acute Kidney Injury for Critically Ill Cardiogenic Shock Patients with Machine Learning Algorithms by Zhang X, Xiong Y, Liu H, Liu Q, Chen S

“…Five machine learning algorithms (LightGBM, decision tree, XGBoost, random forest, and ensemble model) and one conventional logistic regression were applied for the prediction of AKI in critically ill individuals with CS. …”
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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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Machine learning algorithms to predict depression in older adults in China: a cross-sectional study by Yan Li Qing Song, Lin Chen, Haoqiang Liu, Yue Liu

“…Thereafter, the dataset was classified into training and testing sets at a 6:4 ratio. Six ML algorithms, namely, logistic regression, k-nearest neighbors, support vector machine, decision tree, LightGBM, and random forest, were used in constructing a predictive model for depression among the older adult. …”
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Travel Time Prediction in a Multimodal Freight Transport Relation Using Machine Learning Algorithms by Nikolaos Servos, Xiaodi Liu, Michael Teucke, Michael Freitag

“…It requires both sufficient input data, which can be generated, e.g., by mobile sensors, and adequate prediction methods. Machine Learning (ML) algorithms are well suited to solve non-linear and complex relationships in the collected tracking data. …”
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