A study on factors influencing digital sports participation among Chinese secondary school students based on explainable machine learning by XiaoTao Cai, Yi Xian, TongYi Liu, YuXin Zhou, Qing Chen, HaoNan Cui

“…Multilevel logistic regression identified five significant influencing factors (p < 0.05): academic performance, weekly physical education class days, household ICT resources, school ICT resources, and ICT social perception, which were incorporated as features in machine learning models. …”
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Prediction of undernutrition and identification of its influencing predictors among under-five children in Bangladesh using explainable machine learning algorithms. by Md Merajul Islam, Nobab Md Shoukot Jahan Kibria, Sujit Kumar, Dulal Chandra Roy, Md Rezaul Karim

“…Thus, the objectives of this study are to develop an appropriate model for predicting the risk of undernutrition and identify its influencing predictors among under-five children in Bangladesh using explainable machine learning algorithms.…”
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Technological Innovations and Pedagogical Advancements in Basketball Skill Learning: A Systematic Review of High School Physical Education by Andy Nur Abady, Patrick Willyam M Butar Butar Patrick Willyam M Butar Butar, Jet Longakit, Velorine Gordichev Velorine Gordichev

“…Artificial intelligence and machine learning demonstrate significant improvements in tactical skills, with enhancements ranging from 9.655 to 13.989 through generative AI instructional models. …”
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Identifying Key Factors for Predicting the Age at Peak Height Velocity in Preadolescent Team Sports Athletes Using Explainable Machine Learning by Nikolaos-Orestis Retzepis, Alexandra Avloniti, Christos Kokkotis, Maria Protopapa, Theodoros Stampoulis, Anastasia Gkachtsou, Dimitris Pantazis, Dimitris Balampanos, Ilias Smilios, Athanasios Chatzinikolaou

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Predicting the immunological nonresponse to antiretroviral therapy in people living with HIV: a machine learning-based multicenter large-scale study by Suling Chen, Suling Chen, Lixia Zhang, Lixia Zhang, Lixia Zhang, Jingchun Mao, Jingchun Mao, Zhe Qian, Yuanhui Jiang, Yuanhui Jiang, Xinrui Gao, Xinrui Gao, Mingzhu Tao, Mingzhu Tao, Guangyu Liang, Guangyu Liang, Jie Peng, Jie Peng, Shaohang Cai, Shaohang Cai

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Using a seasonal and trend decomposition algorithm to improve machine learning prediction of inflow from the Yellow River, China, into the sea by Shuo Wang, Shuo Wang, Ke Yang, Ke Yang, Hui Peng, Hui Peng

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Association of weight-adjusted waist index and body mass index with chronic low back pain in American adults: a retrospective cohort study and predictive model development based on... by Weiye Zhang, Weiye Zhang, Yan Li, Pengwei Shao, Yuxuan Du, Ke Zhao, Jiawen Zhan, Lee A. Tan

“…Prospective studies are needed to validate causal relationships. The Random Forest machine learning model demonstrated high accuracy in CLBP prediction.…”
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OnmiMHC: a machine learning solution for UCEC tumor vaccine development through enhanced peptide-MHC binding prediction by Fangfang Jian, Haihua Cai, Qushuo Chen, Xiaoyong Pan, Weiwei Feng, Ye Yuan, Ye Yuan

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Radiomic Machine Learning in Invasive Ductal Breast Cancer: Prediction of Ki-67 Expression Level Based on Radiomics of DCE-MRI by Huan Yang MM, Wenxi Wang MM, Zhiyong Cheng MM, Tao Zheng MD, Cheng Cheng MD, Mengyu Cheng MM, Zhanqiu Wang MM

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An Interpretable Machine Learning Framework for Athlete Motor Profiling Using Multi-Domain Field Assessments: A Proof-of-Concept Study by Bartosz Wilczyński, Maciej Biały, Katarzyna Zorena

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Hybridizing spatial machine learning to explore the fine-scale heterogeneity between stunting prevalence and its associated risk determinants in Rwanda by Gilbert Nduwayezu, Ali Mansourian, Jean Pierre Bizimana, Petter Pilesjö

“…Using these datasets, we implemented geographical weighted summary statistics, global random forest, and hybrid random forest model complimented with interpretable machine learning to identify local disparities in the association between stunting prevalence and its related risk factors. …”
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Enhancing safety in surface mine blasting operations with IoT based ground vibration monitoring and prediction system integrated with machine learning by Mangalpady Aruna, Harsha Vardhan, Abhishek Kumar Tripathi, Satyajeet Parida, N. V. Raja Sekhar Reddy, Krishna Moorthy Sivalingam, Li Yingqiu, P. V. Elumalai

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Student’s Digital Intentions Prediction Using CatBoost by Zulwisli, Ambiyar, Muhammad Anwar, Andhika Herayono

“…This advanced machine learning method was applied to data collected from thousands of college students, encompassing various demographic, psychological, and business-related variables. …”
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Recursive feature elimination for summer wheat leaf area index using ensemble algorithm-based modeling: The case of central Highland of Ethiopia by Dereje Biru, Berhan Gessesse, Gebeyehu Abebe

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Machine learning prediction of non-attendance to postpartum glucose screening and subsequent risk of type 2 diabetes following gestational diabetes. by Nishanthi Periyathambi, Durga Parkhi, Yonas Ghebremichael-Weldeselassie, Vinod Patel, Nithya Sukumar, Rahul Siddharthan, Leelavati Narlikar, Ponnusamy Saravanan

“…Women who did not attend postpartum screening appear to have higher metabolic risk and higher conversion to type 2 diabetes by two years post-delivery. Machine learning model can predict women who are unlikely to attend postpartum glucose test using simple antenatal factors. …”
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Using Machine Learning to evaluate Industry 4.0 Maturity: A comprehensive analysis highlighting Lean's impact on Digital Transformation by Oussama Ben Ali

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The Role of Digital Financial Services in Narrowing the Gender Gap in Low–Middle-Income Economies: A Bayesian Machine Learning Approach by Alicia Fernanda Galindo-Manrique, Nuria Patricia Rojas-Vargas

“…A Bayesian regression approach was computed using the Global Findex Database data for 73 countries classified as low and lower-middle-income economies from 2011 to 2022. The Machine Learning approach evaluates the model’s ability to predict women’s autonomy and the role of digital finance. …”
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Machine-learning algorithm to predict home delivery after antenatal care visit among reproductive age women in East Africa by Agmasie Damtew Walle, Shimels Derso Kebede, Jibril Bashir Adem, Daniel Niguse Mamo

“…The RF machine-learning algorithm effectively predicts home delivery. …”
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AI Chatbots in Education: Challenges and Opportunities by Narius Farhad Davar, M. Ali Akber Dewan, Xiaokun Zhang

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Prescriptive Predictors of Mindfulness Ecological Momentary Intervention for Social Anxiety Disorder: Machine Learning Analysis of Randomized Controlled Trial Data by Nur Hani Zainal, Hui Han Tan, Ryan Yee Shiun Hong, Michelle Gayle Newman

“…They completed self-reports of symptoms, risk factors, treatment, and sociodemographics at baseline, posttreatment, and 1-month follow-up (1MFU). Machine learning (ML) models with 17 predictors of optimization to MEMI over SM, defined as a higher probability of SAD remission from MEMI at posttreatment and 1MFU, were evaluated. …”
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