Predictive factors of cardiovascular changes depending on the type and intensity of physical activity in professional athletes by N. P Garganeeva, I. F. Taminova, V. V. Kalyuzhin, E. V Kalyuzhina, I. N. Smirnova

“…To determine the early predictive factors of cardiovascular changes in professional athletes, depending on the type and intensity of physical activity.Material and methods. …”
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Machine Learning-Based Prediction of Feed Conversion Ratio: A Feasibility Study of Using Short-Term FCR Data for Long-Term Feed Conversion Ratio (FCR) Prediction by Xidi Yang, Liangyu Zhu, Wenyu Jiang, Yiting Yang, Mailin Gan, Linyuan Shen, Li Zhu

“…Furthermore, when the testing interval was set to 40 kg and further refined to the range of 50–90 kg, the model achieved an R² of 0.81 and a correlation of 0.90 for FCR prediction in the 30–105 kg range. Among the 19 machine learning algorithms tested, Gradient Boosting, LightGBM, and CatBoost showed superior performance, with Gradient Boosting achieving the best results. …”
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Maternal plasma cell-free RNA as a predictive test for fetal lung maturation by Sean W. D. Carter, Kay Yi Michelle Seah, Si En Poh, Winston Koh, Haruo Usuda, Erin L. Johnson, Yusaku Kumagai, Tsukasa Takahashi, Lara J. Monteiro, Reyna Peñailillo, Gino Nardocci, Hannah R. S. Watson, Masatoshi Saito, Mahesh A. Choolani, Sebastián E. Illanes, Matthew W. Kemp

“…Delivery and ventilation data were analyzed with ANOVA, Tukey HSD, and Dunnett T3 tests. A Random Forest algorithm identified genes that separated mature from immature fetal lung subgroups and determined AUC values for maternal and fetal cell-free RNA (cfRNA) feature sets to predict fetal lung maturation. …”
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Prediction of hearing aid cognitive outcomes in age-related hearing loss by Patrice Voss, Zaida Escila Martinez-Moreno, Francois Prévost, Anthony Zeitouni, Alejandro Lopez Valdes, Alejandro Lopez Valdes, Alejandro Lopez Valdes, Alejandro Lopez Valdes, Etienne de Villers-Sidani

“…We show that several factors can explain large portions of the variance observed in cognitive score changes following short-term hearing aid use in first-time users, suggesting that it might be possible to develop predictive algorithms to determine individualized estimates of the cognitive benefit of hearing aid use. …”
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Credit Scoring Prediction Using Deep Learning Models in the Financial Sector by Xi Shi, Dingfen Tang, Yike Yu

“…Although traditional statistical approaches provide foundational value, they frequently fall short when faced with the task of capturing the nuanced, non-linear associations embedded within extensive datasets, thereby limiting predictive precision. To address these shortcomings, we propose an innovative deep learning paradigm that effectively integrates structured financial information with unstructured behavioral data, thereby bolstering the reliability and accuracy of predictive analytics. …”
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Explainable Artificial Intelligence Models for Predicting Depression Based on Polysomnographic Phenotypes by Doljinsuren Enkhbayar, Jaehoon Ko, Somin Oh, Rumana Ferdushi, Jaesoo Kim, Jaehong Key, Erdenebayar Urtnasan

“…Advanced machine learning algorithms such as random forest, extreme gradient boosting, categorical boosting, and light gradient boosting machines were employed to train and validate the predictive AI models. …”
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Machine learning approaches for predicting energy and exergy efficiency in solar still by Somayeh Davoodabadi Farahani, Mohammad Javad Zarei, Hakan F. Oztop

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Predicting Diabetic Retinopathy and Nephropathy Complications Using Machine Learning Techniques by D. R. Manjunath, J. J. Lohith, S. Selva Kumar, Abhijit Das

“…Diabetes and its complications, especially Diabetic Retinopathy (DR) and Diabetic Nephropathy (DN) is a big challenge to the global healthcare system and needs accurate predictive models to help in early diagnosis and intervention. …”
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Using New Technologies to Analyze Gut Microbiota and Predict Cancer Risk by Mohammad Amin Hemmati, Marzieh Monemi, Shima Asli, Sina Mohammadi, Behina Foroozanmehr, Dariush Haghmorad, Valentyn Oksenych, Majid Eslami

“…Recent advancements in high-throughput sequencing, metagenomics, and machine learning have revolutionized our understanding of the role of gut microbiota in cancer risk prediction. Early detection is made easier by machine learning algorithms that improve the categorization of cancer kinds based on microbiological data. …”
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Predicting outcomes following endovascular aortoiliac revascularization using machine learning by Ben Li, Badr Aljabri, Derek Beaton, Leen Al-Omran, Mohamad A. Hussain, Douglas S. Lee, Duminda N. Wijeysundera, Ori D. Rotstein, Charles de Mestral, Muhammad Mamdani, Mohammed Al-Omran

“…We developed machine learning algorithms that predict 30-day post-procedural outcomes. …”
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Prediction of tuberculosis treatment outcomes using biochemical makers with machine learning by Zheyue Wang, Zhenpeng Guo, Weijia Wang, Qiang Zhang, Suya Song, Yuan Xue, Zhixin Zhang, Jianming Wang

“…Methods Seven feature selection methods and twelve machine learning algorithms were utilized to analyze admission test data from TB patients, identifying predictive features and building prognostic models. …”
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Weighted Content Similarity Feature for Software Architecture Anti-Patterns Prediction by Somayeh Kalhor, Mohammad Reza Keyvanpour

“…So, it is more effective than these two features in predicting dependencies between components using machine learning algorithms.…”
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Machine learning-driven insights into phase prediction for high entropy alloys by Reliance Jain, Sandeep Jain, Sheetal Kumar Dewangan, Lokesh Kumar Boriwal, Sumanta Samal

“…Herein, a method of designing substitutional high entropy alloys with optimization of input features and predict their phase formation, using different ML algorithms are proposed. …”
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CAREUP: An Integrated Care Platform with Intrinsic Capacity Monitoring and Prediction Capabilities by Marcin Kolakowski, Andrea Lupica, Seif Ben Bader, Vitomir Djaja-Josko, Jerzy Kolakowski, Jacek Cichocki, Jaouhar Ayadi, Luca Gilardi, Angelo Consoli, Irina Georgiana Mocanu, Oana Cramariuc, Lionello Ferrazzini, Eva Reithner, Magdalena Velciu, Barbara Borgogni, Sofia Rivaira, Sara Leonzi, Giacomo Cucchieri, Vera Stara

“…Besides standard functionalities like storing health measurement data or providing users with personalized recommendations, the platform includes novel intrinsic capacity assessment and prediction algorithms. Older adults’ performance is continuously monitored in all five IC domains—locomotion, psychology, cognition, vitality, and sensory capacity—based on measurement results and answers to questionnaires gathered using the platform’s mobile applications. …”
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Bayesian Model Prediction for Breast Cancer Survival: A Retrospective Analysis by Islam Bani Mohammad, Muayyad M. Ahmad

“…Objective: Over the recent years, machine learning (ML) models have been increasingly used in predicting breast cancer survival because of improvements in ML algorithms. …”
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Fall Risk Prediction Using Instrumented Footwear in Institutionalized Older Adults by Huanghe Zhang, Chuanyan Wu, Yulong Huang, Rui Song, Damiano Zanotto, Sunil K. Agrawal

“…The importance of each type of data is assessed using a brute-force search method, through which the optimal features are selected. AdaBoost algorithms are then utilized to develop predictive models based on the selected features. …”
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Feasibility of machine learning–based modeling and prediction to assess osteosarcoma outcomes by Qinfei Zhao, Weiquan Hu, Yu Xia, Shengyun Dai, Xiangsheng Wu, Jing Chen, Xiaoying Yuan, Tianyu Zhong, Xuxiang Xi, Qi Wang

“…However, identifying robust gene signatures to predict osteosarcoma outcomes remains a significant challenge. …”
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SMART DELAY PREDICTION: SUPERVISED MACHINE LEARNING SOLUTIONS FOR CONSTRUCTION PROJECTS by Pramodini Sahu, Dillip Kumar Bera, Pravat Kumar Parhi, Meenakshi Kandpal

“…Conventional techniques for predicting delays often do not deliver concrete predictions due to the multiplicity and dynamic character of construction tasks. …”
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Intelligent prediction and oriented design of high-hardness high-entropy ceramics by Anzhe Wang, Jicheng Liu, Linwei Guo, Kejie Qu, Haishen Xie, Yawei Li, Bin Du

“…This work utilizes machine learning and heuristic optimization algorithms to achieve accurate predictions of bulk high-entropy ceramics hardness (with validation set errors <10 %) and the oriented design of high-entropy ceramics with a hardness of 25 GPa (with an average error of 2.6 %). …”
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Developing advanced datadriven framework to predict the bearing capacity of piles on rock by Kennedy C. Onyelowe, Shadi Hanandeh, Viroon Kamchoom, Ahmed M. Ebid, Fabián Danilo Reyes Silva, José Luis Allauca Palta, José Luis Llamuca Llamuca, Siva Avudaiappan

“…This research presents an advanced data-driven framework that integrates multiple machine learning algorithms to predict the bearing capacity of piles based on geotechnical and in-situ test parameters. …”
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