Evaluation of an Interdisciplinary Educational Program to Foster Learning Health Systems: Education Evaluation by Sathana Dushyanthen, Nadia Izzati Zamri, Wendy Chapman, Daniel Capurro, Kayley Lyons

“…The course covered a number of topics including background on LHS, establishing learning communities, the design thinking process, data preparation and machine learning analysis, process modeling, clinical decision support, remote patient monitoring, evaluation, implementation, and digital transformation. …”
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Machine Learning and Neural Networks for IT-Diagnostics of Neurological Diseases by U. A. Vishniakou, Y. W. Xia, Ch. Y. Yu

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Machine learning-based prediction of optimal antenatal care utilization among reproductive women in Nigeria by Jamilu Sani, Adeyemi Oluwagbemiga, Mohamed Mustaf Ahmed

“…Machine learning (ML) offers a promising alternative that uncovers hidden patterns and improves prediction accuracy. …”
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Machine learning models for predicting metabolic dysfunction-associated steatotic liver disease prevalence using basic demographic and clinical characteristics by Gangfeng Zhu, Yipeng Song, Zenghong Lu, Qiang Yi, Rui Xu, Yi Xie, Shi Geng, Na Yang, Liangjian Zheng, Xiaofei Feng, Rui Zhu, Xiangcai Wang, Li Huang, Yi Xiang

“…Using eight demographic and clinical characteristics (age, educational level, height, weight, waist and hip circumference, and history of hypertension and diabetes), we built predictive models for MASLD (classified as none or mild: controlled attenuation parameter (CAP) ≤ 269 dB/m; moderate: 269–296 dB/m; severe: CAP > 296 dB/m) employing 10 machine learning algorithms: logistic regression (LR), multilayer perceptron (MLP), extreme gradient boosting (XGBoost), bootstrap aggregating, decision tree, K-nearest neighbours, light gradient boosting machine, naive Bayes, random forest, and support vector machine. …”
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Learning models for predicting pavement friction based on non-contact texture measurements: Comparative assessment by Xiuquan Lin, You Zhan, Zilong Nie, Joshua Qiang Li, Xinyu Zhu, Allen A. Zhang

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Unveiling the Impact of Socioeconomic and Demographic Factors on Graduate Salaries: A Machine Learning Explanatory Analytical Approach Using Higher Education Statistical Agency Dat... by Bassey Henshaw, Bhupesh Kumar Mishra, William Sayers, Zeeshan Pervez

“…This study investigates determinants of graduate salaries in the UK, utilising survey data from HESA (Higher Education Statistical Agency) and integrating advanced machine learning (ML) explanatory techniques with statistical analytical methodologies. …”
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A Practice-Oriented Computational Thinking Framework for Teaching Neural Networks to Working Professionals by Jing Tian

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A Cognitive Topic Modeling Approach for Detailed Learner Analysis in Asynchronous Discussions by Ruofei Ding, Xianfei Luo, Shumin Chen, Xiaomin Wu, Chi Zhang, Lixiang Liu, Yanyu Huang, Ling Chen, Jingxiu Huang, Yunxiang Zheng, Ke Zhu

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Integrating Learning Analytics and Collaborative Learning for Improving Student’s Academic Performance by Adnan Rafique, Muhammad Salman Khan, Muhammad Hasan Jamal, Mamoona Tasadduq, Furqan Rustam, Ernesto Lee, Patrick Bernard Washington, Imran Ashraf

“…To support such changes, a visualization system is also developed to track and monitor the performance of students, groups, and overall class to help teachers in the regrouping of students concerning their performance. Several well-known machine learning models are applied to predict students performance. …”
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Advanced Machine Learning Techniques for Energy Consumption Analysis and Optimization at UBC Campus: Correlations with Meteorological Variables by Amir Shahcheraghian, Adrian Ilinca

“…This study is presented as a solution to these challenges through a detailed analysis of energy consumption across UBC Campus buildings using a variety of machine learning models, including Neural Networks, Decision Trees, Random Forests, Gradient Boosting, AdaBoost, Linear Regression, Ridge Regression, Lasso Regression, Support Vector Regression, and K-Neighbors. …”
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Predictions of Multilevel Linguistic Features to Readability of Hong Kong Primary School Textbooks: A Machine Learning Based Exploration by Zhengye Xu, Yixun Li, Duo Liu

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A machine learning-based approach to predict depression in Chinese older adults with subjective cognitive decline: a longitudinal study by Jing Xu, Wenjin Zhang, Wenli Liu

“…Consequently, we performed feature importance analysis using both Boosted XGBoost and RF models. The results identified five significant predictors of depression in older adults with subjective cognitive decline (SCD): educational attainment, digestive health status, arthritis diagnosis, sleep duration, and residential location.The machine learning model developed in our study demonstrates strong predictive performance for depression risk among older adults with subjective cognitive decline (SCD), enabling early identification of high-risk individuals. …”
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A comprehensive model for concrete strength prediction using advanced learning techniques by Sagar Dhengare, Udaykumar Waghe, Ganesh Yenurkar, Anjana Shyamala

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Advancing Knowledge on Machine Learning Algorithms for Predicting Childhood Vaccination Defaulters in Ghana: A Comparative Performance Analysis by Eliezer Ofori Odei-Lartey, Stephaney Gyaase, Dominic Asamoah, Thomas Gyan, Kwaku Poku Asante, Michael Asante

“…The key predictors identified include timely receipt of birth and week six vaccines, the child’s age, household wealth index, and maternal education. The findings demonstrate that robust machine learning frameworks, combined with temporal and contextual feature engineering, can improve defaulter risk prediction accuracy. …”
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Explainable machine learning model for predicting internal mammary node metastasis in breast cancer: Multi-method development and cross-cohort validation by Yirong Xiang, Jian Tie, Siyuan Zhang, Chen Shi, Changkuo Guo, Yushuo Peng, Zhaoqing Fan, Weihu Wang

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AHP-Guided Stacked Ensemble Modeling for Student Engagement Level Prediction in Online Education by Jingjing Fu, Linjie Luo, Zhifeng Zhong, Jiaming Qin

“…Third, the use of an ensemble stack based on classical machine learning models, including K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Naive Bayes (NB). …”
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Predicting depression in healthy young adults: A machine learning approach using longitudinal neuroimaging data by Ailing Zhang, Haobo Zhang

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Machine learning-based exploration of the associations between multiple minerals' intake and thyroid dysfunction: data from the National Health and Nutrition Examination Survey by Shaojie Liu, Shaojie Liu, Weibin Huang, Yaming Lin, Yifei Wang, Hongjin Li, Xiaojuan Chen, Yijia Zou, Bo Chen, Baochang He, Zhiping Yang, Jing Fan

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Utilizing machine learning techniques to identify severe sleep disturbances in Chinese adolescents: an analysis of lifestyle, physical activity, and psychological factors by Lirong Zhang, Shaocong Zhao, Wei Yang, Zhongbing Yang, Zhi’an Wu, Hua Zheng, Mingxing Lei, Mingxing Lei, Mingxing Lei

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Comprehensive Investigation of Machine Learning and Deep Learning Networks for Identifying Multispecies Tomato Insect Images by Chittathuru Himala Praharsha, Alwin Poulose, Chetan Badgujar

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