A comprehensive review of predictive analytics models for mental illness using machine learning algorithms by Md. Monirul Islam, Shahriar Hassan, Sharmin Akter, Ferdaus Anam Jibon, Md. Sahidullah

“…This study reviews the machine learning models, algorithms, and applications for the early detection of mental disease, particularly emphasizing the data modalities. …”
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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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Research and Application of Denitrification Optimal Control Based on Multi-Variable Generalized Predictive Control Algorithms by Xiaodong ZHANG, Pengfei WANG, Le LIU

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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 depression in older adults in China: a cross-sectional study by Yan Li Qing Song, Lin Chen, Haoqiang Liu, Yue Liu

“…Self-rated health, nighttime sleep, gender, age, and cognitive function are the five most important characteristics of all models in terms of predicting the occurrence of depression.ConclusionThe occurrence of depression among China's older adult population and the critical factors leading to depression can be predicted and identified, respectively, by ML algorithms.…”
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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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Evaluating Label Encoding and Preprocessing Techniques for Breast Cancer Prediction Using Machine Learning Algorithms by Mukesh Kumar, Vivek Bhardwaj

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A Comprehensive Review of Artificial Intelligence-Based Algorithms for Predicting the Remaining Useful Life of Equipment by Weihao Li, Jianhua Chen, Sijuan Chen, Peilin Li, Bing Zhang, Ming Wang, Ming Yang, Jipu Wang, Dejian Zhou, Junsen Yun

“…While significant advancements in computer hardware and artificial intelligence (AI) algorithms have catalyzed substantial progress in AI-based RUL prediction, extant research frequently exhibits a narrow focus on specific algorithms, neglecting a comprehensive and comparative analysis of AI techniques across diverse equipment types and operational scenarios. …”
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A Clinical Risk Prediction Model for Depressive Disorders Based on Seven Machine Learning Algorithms by Jin W, Chen S, Wang M, Lin P

“…Univariate logistic regression analysis (p< 0.1) was initially performed to identify potential predictors, followed by feature selection using the Boruta and LASSO algorithms. Seven machine learning algorithms were employed to construct predictive models, with their performance evaluated using metrics such as AUC, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), precision, recall, and F1 score. …”
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CLASSIFICATION AND PREDICTION OF BENTHIC HABITAT FROM SCIENTIFIC ECHOSOUNDER DATA: APPLICATION OF MACHINE LEARNING ALGORITHMS by Baigo HAMUNA, Sri PUJIYATI, Jonson Lumban GAOL, Totok HESTIRIANOTO

“…The classification and prediction process of benthic habitats uses two machine learning algorithms, Random Forest (RF) and Support Vector Machine (SVM), in XLSTAT Basic+ software. …”
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Hybridization of Machine Learning Algorithms and an Empirical Regression Model for Predicting Debris-Flow-Endangered Areas by Xiang Wang, Mi Tian, Qiang Qin, Jingwei Liang

“…This paper proposes a hybrid method for predicting debris-flow hazard zone by integrating machine-learning algorithms and an empirical regression model. …”
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Prediction of Gasification Process via Random Forest Regression Model Optimized with Meta-Heuristic Algorithms by Eunsung Oh

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Final weight prediction from body measurements in Kıvırcık lambs using data mining algorithms by Ö. Şengül, Ş. Çelik

“…The statistical performances of these algorithms (CHAID, exhaustive CHAID, CART, RF, MARS, and Bagging MARS) were tested by using several goodness-of-fit criteria, namely the coefficient of determination (<span class="inline-formula"><i>R</i>²=0.699</span>, 0.699, 0.722, 0.662, 0.792, and 0.624), adjusted coefficient of determination (Adj.…”
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Optimizing Server Load Distribution in Multimedia IoT Environments through LSTM-Based Predictive Algorithms by Somaye Imanpour, AhmadReza Montazerolghaem, Saeed Afshari

“…The Long Short-Term Memory (LSTM) prediction algorithm is employed to accurately estimate server loads and fuzzy systems are integrated to optimize load distribution across servers. …”
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Heating Load Prediction with Meta-Heuristic ‎Algorithms and Adaptive Neuro-Fuzzy ‎Inference System Integration by Seyed Hadi Seyed Hatami, Reza Seifi Majdar

“…Addressing this need, this study adopts a holistic ap-proach by integrating advanced optimization algorithms with precise heating load prediction techniques. …”
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Optimisation of Ensemble Learning Algorithms for Geotechnical Applications: A Mathematical Approach to Relative Density Prediction by Mahdy Khari, Ali Dehghanbanadaki, Danial Jahed Armaghani, Manoj Khandelwal

“…The challenge of predicting relative dry density (Dr) in granular materials is addressed through advanced mathematical modelling and machine learning (ML) techniques. …”
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Energy management of microgrid coalitions considering renewable energy prediction based on machine learning algorithms by Negar Dehghani Mahmoudabadi, Mehran Khalaj, Davood Jafari, Ali Taghizadeh Herat, Parisa Mousavi Ahranjani

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Prediction of Surface Settlement Induced by Large-Diameter Shield Tunneling Based on Machine-Learning Algorithms by Chao Li, Jinhui Li, Zhongqi Shi, Li Li, Mingxiong Li, Dianqi Jin, Guo Dong

“…Among the three machine-learning algorithms, the LSTM algorithm gives the best accuracy in predicting the maximum surface settlement and can effectively predict the settlement development in different strata.…”
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Predictive Modeling of Credit Card Rejection Using Machine Learning Algorithms: A Comparative Study by Shengyu Gu

“…The investigation also involves optimization methodologies such as Pelican Optimization Algorithm (POA), Coot Optimization Algorithm (COA), and Chimp Optimization Algorithm (CHOA). …”
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