Exploring malaria prediction models in Togo: a time series forecasting by health district and target group by Muriel Rabilloud, Nicolas Voirin, Anne Thomas, Tchaa Abalo Bakai, Tinah Atcha-Oubou, Tchassama Tchadjobo

“…Objectives Integrating malaria prediction models into malaria control strategies can help to anticipate the response to seasonal epidemics. …”
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506 Evaluating prediction models for conversion of clinically isolated syndrome to multiple sclerosis: A systematic review by Mei-An Nolan, Danielle Howard, James Beck

“…Objectives/Goals: Accurately stratifying patients with clinically isolated syndrome by risk of developing multiple sclerosis is of great clinical importance. Though numerous prediction models attempt to achieve this goal, no systematic review exists to independently evaluate these models. …”
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Triage Data-Driven Prediction Models for Hospital Admission of Emergency Department Patients: A Systematic Review by Hyun A Shin, Hyeonji Kang, Mona Choi

“…Data extraction adhered to the CHARMS (Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies), and the risk of bias was evaluated using PROBAST (Prediction model Risk of Bias Assessment Tool). …”
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Automated acute pain prediction in domestic goats using deep learning-based models on video-recordings by Ludovica Chiavaccini, Anjali Gupta, Nicole Anclade, Guido Chiavaccini, Chiara De Gennaro, Alanna N. Johnson, Diego A. Portela, Marta Romano, Enzo Vettorato, Daniela Luethy

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Prediction models for Mtb infection among adolescent and adult household contacts in high tuberculosis incidence settings. by Edson Tawanda Marambire, Claire J Calderwood, Leyla Larsson, Kathrin Held, Palwasha Khan, Denise Banze, Celina Nhamuave, Lillian T Minja, Alfred Mfinanga, Rishi K Gupta, Celso Khosa, Junior Mutsvangwa, Norbert Heinrich, Katharina Kranzer

“…Development of Mtb infection prediction models to guide clinical decision-making aims to overcome these challenges. …”
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Enhancing Pollen Prediction in Beijing, a Chinese Megacity: Leveraging Ensemble Learning Models for Greater Accuracy by Wenxi Ruan, Ziming Li, Zhaobin Sun, Xingqin An, Yuxin Zhao, Shuwen Zhang, Yinglin Liang, Yaqin Bu, Jingyi Xin, Xiaoyi Hang

“…Notably, the prediction accuracy of Neural NetTorch significantly improves with extended prediction time, with its R2 increasing from 0.34 to 0.67 as the prediction period extends from one day to three days. …”
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Ensemble boosting-based soft-computing models for predicting the bond strength between steel and CFRP plate by Irwan Afriadi, Chanachai Thongchom, Divesh Ranjan Kumar, Suraparb Keawsawasvong, Warit Wipulanusat

“…For the machine learning boosting-based model approach, eight total input variables and one output variable were chosen to predict the maximum load (PU) of the bonding behavior between the CFRP and steel. …”
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A Multidimensional Parameter Dynamic Evolution-Based Airdrop Target Prediction Method Driven by Multiple Models by Xuesong Wang, Jiapeng Yin, Jianbing Li, Yongzhen Li

“…To evaluate the impact of model input errors on prediction methods, this work analyzes the influence mechanism of the wind field detection error on the airdrop prediction method via the Relative Gain Array (RGA) and verifies the analytical results using the numerical simulation method. …”
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Advanced hybrid machine learning models with explainable AI for predicting residual friction angle in clay soils by Mawuko Luke Yaw Ankah, Shalom Adjei-Yeboah, Yao Yevenyo Ziggah, Edmund Nana Asare

“…This study explores three advanced hybrid machine learning models: Gradient Boosting Neural Network (GrowNet), Reinforcement Learning Gradient Boosting Machine (RL-GBM), and a Stacking Ensemble to predict the residual friction angle of clay soils, addressing a critical gap in current predictive methodologies. …”
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Establishment of two pathomic-based machine learning models to predict CLCA1 expression in colon adenocarcinoma. by Caiyun Yao, Maotong Hu, Lingxia Zhou, Hui Chen, Yang Cao

“…Two pathomics-based machine learning models were developed to predict CLCA1 expression from H&E stained images of COAD. …”
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Predicting Pharmacokinetics of Active Constituents in <i>Spatholobi caulis</i> by Using Physiologically Based Pharmacokinetic Models by Xiaoyan Liu, Ruihu Du, Tao Zhang, Yingzi Li, Ludi Li, Zheng Yang, Youbo Zhang, Qi Wang

“…These results confirm the successful establishment of PBPK models of these four constituents from SPC in this study, and these models were applicable to predict pharmacokinetics across various doses and extrapolate across species. …”
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Digital Assets and the Global Economy: How the Use of Statistical Models Can Help Bitcoin Price Prediction by L. P. Bakumenko, N. S. Vasileva

“…The purpose of the study is to analyze the potential of statistical modeling in predicting the prices of the Bitcoin cryptocurrency and its impact on the economy. …”
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Development of cardiovascular and all-cause mortality risk prediction models for maintenance hemodialysis patients based on metabolomics by Lian-Lian You, Cui Dong, Zhi-Hong Wang, Shuang Zhang, Yu Zhang, Ting-Ting Kuai, Jia Xiao, Shu-Xin Liu, Qing-Cheng Zeng

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Prediction of Banks Efficiency Using Feature Selection Method: Comparison between Selected Machine Learning Models by Hamzeh F. Assous

“…Next, the feature selection is applied for different prediction models. Subsequently, 4 prediction models (i.e., SVM, CHAID, linear regression, and a neural network) were developed to choose the best fit. …”
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Classification of primary glomerulonephritis using machine learning models: a focus on IgA nephropathy prediction by Zhengbiao Hu, Shuangshan Bu, Kai Wang, Qianqian Cao, Huanhuan Zheng, Jie Yang, Shanshan Chen, Yuemeng Wu, Wenkai Ren, Chenlei He

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Predicting Win-rates of Hearthstone Decks: Models and Features that Won AAIA’2018 Data Mining Challenge by Quang Hieu Vu, Dymitr Ruta, Andrzej Ruta, Ling Cen

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Optimized Machine Learning Models for Enhanced Stock Market Predictions: A Case Study on the SSE Index by Vahid Babazadeh, Ahmad Faramarzi, Ali Rahnamaei

“…This study aims to introduce a machine learning-based model for Shanghai Stock Exchange Index (SSE) index prediction. …”
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Aspiration risk prediction models in patients with nasogastric enteral nutrition: a systematic review and meta-analysis by Juan Chen, Yushuang Su, Tianlong Li, Xiaorong Mao, Qinghua Jiang, Qin Yang, Qing Wen, Zaichun Pu, Mengting Liu

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Establishment of two pathomic-based machine learning models to predict CLCA1 expression in colon adenocarcinoma by Caiyun Yao, Maotong Hu, Lingxia Zhou, Hui Chen, Yang Cao

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Enhancing customer retention with machine learning: A comparative analysis of ensemble models for accurate churn prediction by Payam Boozary, Sogand Sheykhan, Hamed GhorbanTanhaei, Cosimo Magazzino

“…This paper investigates the use of machine learning models for customer churn prediction, focusing on the comparative effectiveness of ensemble approaches such as XGBoost and Random Forest with classical classifiers. …”
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