Epidemiological association and machine learning-based prediction of lung cancer risk linked to long-term lagged satellite-derived PM2.5 in China by Feiran Wei, Shijun Yang, Huiying Wang, Meng Zhao, Meng Zhao, Jinyi Zhou, Xiaobing Shen, Xiaobing Shen, Renqiang Han, Renqiang Han, Gaoqiang Fei

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Biomarker discovery and development of prognostic prediction model using metabolomic panel in breast cancer patients: a hybrid methodology integrating machine learning and explaina... by Fatma Hilal Yagin, Yasin Gormez, Fahaid Al-Hashem, Irshad Ahmad, Fuzail Ahmad, Luca Paolo Ardigò

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Interpretable machine learning analysis of environmental characteristics on bacillary dysentery in Sichuan Province by Yao Zhang, Qiao-Lin Wang, Wei Peng, Meng-Yuan Zhang, Yao Qin, Lun Zhang, Rong-Jie Wei, Dian-Ju Kang

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PV Module Soiling Detection Using Visible Spectrum Imaging and Machine Learning by Boris I. Evstatiev, Dimitar T. Trifonov, Katerina G. Gabrovska-Evstatieva, Nikolay P. Valov, Nicola P. Mihailov

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Machine learning algorithm to predict in-hospital mortality after aneurysmal subarachnoid hemorrhage by Juri V. Kivelev, Alexey L. Krivoshapkin, Albert A. Sufianov

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Prediction of Myopia Among Undergraduate Students Using Ensemble Machine Learning Techniques by Isteaq Kabir Sifat, Tajin Ahmed Jisa, Jyoti Shree Roy, Nourin Sultana, Farhana Hasan, Md Parvez Mosharaf, Md. Kaderi Kibria

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Support of individual educational trajectories based on the concept of explainable artificial intelligence by I. G. Zakharova, M. S. Vorobeva, Yu. V. Boganyuk

“…The methods of intellectual analysis of texts in natural language were employed for preliminary processing of source documents. To predict educational outcomes, the authors used clustering, classification and regression models created through applying machine learning methods.Results. …”
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Application of Machine Learning for Academic Outcome Prediction: A Methodological Comparative Study by Md. Wira Putra Dananjaya, Putu Gita Pujayanti

“…This research conducts a methodological comparative analysis of five machine learning models Simple Linear Regression, Multiple Linear Regression (MLR), Decision Tree, Random Forest, and Artificial Neural Network (ANN) to determine the most accurate predictive approach using a comprehensive dataset encompassing academic, behavioral, and psychosocial factors. …”
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Acoustic-based machine learning approaches for depression detection in Chinese university students by Yange Wei, Yange Wei, Shisen Qin, Fengyi Liu, Rongxun Liu, Yunze Zhou, Yuanle Chen, Xingliang Xiong, Wei Zheng, Guangjun Ji, Yong Meng, Fei Wang, Fei Wang, Ruiling Zhang

“…Further, 27 acoustic features (10 spectral features, 3 prosodic features, and 1 glottal features) were significantly correlated with depression severity. Among five machine learning algorithms, LDA model demonstrated the highest classification performance, with an AUC of 0.771. …”
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Machine Learning-Driven Prediction of Vitamin D Deficiency Severity with Hybrid Optimization by Usharani Bhimavarapu, Gopi Battineni, Nalini Chintalapudi

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A machine learning approach to predict self-efficacy in breast cancer survivors by İsmail Toygar, Su Özgür, Gülcan Bağçivan, Ezgi Karaçam, Hilal Benzer, Ferda Akyüz Özdemir, Halise Taşkın Duman, Özlem Ovayolu

“…AUC values were used as ranker for the machine learning models. The ranks of the models were as follows; logistic regression model (0.715), RF (0.710), SVM (0.704), and XGBoost (0.694). …”
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Supervised machine learning for microbiomics: Bridging the gap between current and best practices by Natasha Katherine Dudek, Mariami Chakhvadze, Saba Kobakhidze, Omar Kantidze, Yuriy Gankin

“…Machine learning (ML) is poised to drive innovations in clinical microbiomics, such as in disease diagnostics and prognostics. …”
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Prediction of depressive disorder using machine learning approaches: findings from the NHANES by Thien Vu, Research Dawadi, Masaki Yamamoto, Jie Ting Tay, Naoki Watanabe, Yuki Kuriya, Ai Oya, Phap Ngoc Hoang Tran, Michihiro Araki

“…Methods This study utilized data from the National Health and Nutrition Examination Survey (NHANES) 2013–2014 to predict depression using six supervised ML models: Logistic Regression, Random Forest, Naive Bayes, Support Vector Machine (SVM), Extreme Gradient Boost (XGBoost), and Light Gradient Boosting Machine (LightGBM). …”
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Spectral Analysis of Light-Adapted Electroretinograms in Neurodevelopmental Disorders: Classification with Machine Learning by Paul A. Constable, Javier O. Pinzon-Arenas, Luis Roberto Mercado Diaz, Irene O. Lee, Fernando Marmolejo-Ramos, Lynne Loh, Aleksei Zhdanov, Mikhail Kulyabin, Marek Brabec, David H. Skuse, Dorothy A. Thompson, Hugo Posada-Quintero

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Early Detection of Alzheimer’s Disease: An Extensive Review of Advancements in Machine Learning Mechanisms Using an Ensemble and Deep Learning Technique by Renjith Prabhavathi Neelakandan, Ramesh Kandasamy, Balasubramani Subbiyan, Mariya Anto Bennet

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Opinion mining in e-commerce: Evaluating machine learning approaches for sentiment analysis by L. Lakshmi, Ali B.M. Ali, K Dhana Sree Devi, Muhammad Rafiq, Iskandar Shernazarov, Nashwan Adnan Othman, M. Ijaz Khan

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Use of machine learning to predict creativity among nurses: a multidisciplinary approach by Rola H. Mudallal, Majd T. Mrayyan, Mohammad Kharabsheh

“…Researchers are encouraged to use machine learning models because they achieve good prediction performance with high precision. …”
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Digital Mapping of Soil Equivalent Calcium Carbonate Using Landsat 8 Satellite Images and Environmental Data by Machine Learning Models in Badr Watershed, Kurdistan Province by M. Zarinibahador

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Simulation of Active Layer Thickness Based on Multi-Source Remote Sensing Data and Integrated Machine Learning Models: A Case Study of the Qinghai-Tibet Plateau by Guoyu Wang, Shuting Niu, Dezhao Yan, Sihai Liang, Yanan Su, Wei Wang, Tao Yin, Xingliang Sun, Li Wan

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Using Life’s Essential 8 and heavy metal exposure to determine infertility risk in American women: a machine learning prediction model based on the SHAP method by Xiaoqing Gu, Qianbing Li, Xiangfei Wang

“…The association between LE8 and heavy metal exposure and risk of infertility was assessed using logistic regression analysis and six machine learning models (Decision Tree, GBDT, AdaBoost, LGBM, Logistic Regression, Random Forest), and the SHAP algorithm was used to explain the model’s decision process.ResultsOf the six machine learning models, the LGBM model has the best predictive performance, with an AUROC of 0.964 on the test set. …”
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