Machine Learning-Based Prediction Model for Predicting the Effect of the Serum γKlotho Level on Susceptibility to Coronary Heart Disease by Guo ZT, Yu XL, Cheng H, Naman T

“…Zi-Tong Guo,1 Xiao-Lin Yu,2 Hui Cheng,2 Tuersunjiang Naman2 1Department of Cardiology, First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang, People’s Republic of China; 2Department of Cardiology, People’s Hospital of Xinjiang Uygur Autonomous Region, Urumqi, Xinjiang, People’s Republic of ChinaCorrespondence: Tuersunjiang Naman, Email tursunjan1016@163.comObjective: This study investigates the relationship between serum γKlotho levels and coronary heart disease (CHD) risk and develops a machine learning model for CHD prediction.Methods: A total of 1435 subjects were enrolled for analysis and randomized as training (n =  969, 70%) or validation (n =  466, 30%) group. …”
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Brachial artery constriction during brachial artery reactivity testing predicts major adverse clinical outcomes in women with suspected myocardial ischemia: results from the NHLBI-... by Tara L Sedlak, B Delia Johnson, Carl J Pepine, Steven E Reis, C Noel Bairey Merz

“…<h4>Objectives</h4>To determine whether BAC predicts adverse CV outcomes and/or mortality in the women's ischemic Syndrome Evaluation Study (WISE). …”
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Automatic Movement Recognition for Evaluating the Gross Motor Development of Infants by Yin-Zhang Yang, Jia-An Tsai, Ya-Lan Yu, Mary Hsin-Ju Ko, Hung-Yi Chiou, Tun-Wen Pai, Hui-Ju Chen

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Evaluating the causal effects of life-course adiposity on jaw anomalies by Xin Chen, Zheng Cheng, Qianyi Wang, Yubin Jiang, Qing Cheng, Qianglin Jiang

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IDRdecoder: a machine learning approach for rational drug discovery toward intrinsically disordered regions by Clara Shionyu-Mitusyama, Satoshi Ohmori, Subaru Hirata, Hirokazu Ishida, Tsuyoshi Shirai, Tsuyoshi Shirai

“…The performance was compared with existing methods including ProteinBERT, and IDRdecoder demonstrated moderately improved performance.DiscussionIDRdecoder is the first application for predicting drug interaction sites and ligands in IDR sequences. …”
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An Overview of the Empirical Evaluation of Explainable AI (XAI): A Comprehensive Guideline for User-Centered Evaluation in XAI by Sidra Naveed, Gunnar Stevens, Dean Robin-Kern

“…Various approaches, including new concepts, models, and user interfaces, aim to improve explainability, build user trust, enhance satisfaction, and increase task performance. Evaluation research has emerged to define and measure the quality of these explanations, differentiating between formal evaluation methods and empirical approaches that utilize techniques from psychology and human–computer interaction. …”
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Machine learning techniques for predictive modelling in geotechnical engineering: a succinct review by Shrikant M. Harle, Rajan L. Wankhade

“…Techniques such as aRVM, Random Forest (RF), PSO-ANN, Support Vector Machines (SVM), and numerical methods are discussed for their effectiveness in predicting settlement, building responses, and safety risks. …”
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EVALUATING TURKEY HUNTER ATTITUDES ON WILDLIFE MANAGEMENT AREAS IN MISSISSIPPI by Douglas A. Little, Jacob L. Bowman, George A. Hurst, Ronald S. Seiss, Donna L. Minnis

“…Respondent attitudes towards 8 current and potential regulations were evaluated. Respondents supported controlling the number of hunters during periods of high hunting pressure (57%), closing some roads to restrict access by motorized vehicles (79%), protecting juvenile males (jakes) from harvest (87%), and the method of protecting jakes under the current regulations which allow the harvest of adult males or males with a ≥6‐in beard (91%).…”
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Predictive Performance of Machine Learning for Suicide in Adolescents: Systematic Review and Meta-Analysis by Lingjiang Liu, Zhiyuan Li, Yaxin Hu, Chunyou Li, Shuhan He, Shibei Zhang, Jie Gao, Huaiyi Zhu, Guoping Huang

“…MethodsThis review assessed ML for predicting adolescent suicide–related behaviors. …”
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Predicting hepatocellular carcinoma survival with artificial intelligence by İsmet Seven, Doğan Bayram, Hilal Arslan, Fahriye Tuğba Köş, Kübranur Gümüşlü, Selin Aktürk Esen, Mücella Şahin, Mehmet Ali Nahit Şendur, Doğan Uncu

“…The aim of this research was to evaluate the ability of machine learning (ML) methods in predicting the survival probability of HCC patients. …”
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Data Mining Classification Techniques for Diabetes Prediction by Hindreen Rashid Abdulqadir, Adnan Mohsin Abdulazeez, Dilovan Assad Zebari

“…Diabetes may be predicted and prevented by exploring critical diabetes characteristics by computational data extraction methods. …”
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The Predictive Value of Preoperative Systemic Immune-Inflammation Index in Patients with Granulomatous Mastitis by Ouyang L, Qin J, Cui T, Tan Y

“…We aimed to investigate the potential predictive value of the SII in the prognosis of granulomatous mastitis (GM).Patients and Methods: We enrolled 245 patients with GM who underwent surgery between 2015 and 2020 in this study. …”
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Link Predictions with Bi-Level Routing Attention by Yu Wang, Shu Xu, Zenghui Ding, Cong Liu, Xianjun Yang

“…Manual completion of KGs is time-consuming and costly, emphasizing the importance of developing automated methods for KGC. Link prediction serves as a fundamental task in this domain. …”
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New system for predicting the outcome of secondary peritonitis by Nikolay V. Lebedev, Sariya B. Agrba, Vasily S. Popov, Alexey E. Klimov, Giorgy T. Svanadze

“…We analyzed the effectiveness of several systems of predicting the peritonitis outcomes: the Mannheim’s Peritoneal Index (MPI), World Society for Emergency Surgery Sepsis Severity Score (WSES SSS), Acute Physiology and Chronic Health Evaluation II (APACHE II) system, general Sequential Organ Failure Assessment Score (gSOFA), as well as the Peritonitis Prognosis System (PPS) developed by the authors. …”
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Optimizing Flight Delay Predictions with Scorecard Systems by Ilona Jacyna-Gołda, Krzysztof Cur, Justyna Tomaszewska, Karol Przanowski, Sarka Hoskova-Mayerova, Szymon Świergolik

“…The model was validated using test datasets, and predictive performance was evaluated by comparing forecast delays with actual results. …”
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Pedestrian Vision Language Model for Intentions Prediction by Farzeen Munir, Shoaib Azam, Tsvetomila Mihaylova, Ville Kyrki, Tomasz Piotr Kucner

“…Furthermore, to complement our PedVLM model and further facilitate research, we also publicly release the corresponding dataset, PedPrompt, which includes the prompts in the Question-Answer (QA) template for pedestrian intention prediction. PedVLM is evaluated on PedPrompt, JAAD, and PIE datasets demonstrates its efficacy compared to state-of-the-art methods. …”
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STVMamba: precipitation nowcasting with spatiotemporal prediction model by Maoyang Zou, Longrui Wen, Yuanyuan Huang, Yuan He, Jingzhong Xiao

“…Abstract A lightweight rainfall nowcasting model is required by Sichuan provincial meteorological bureaus. Deep learning methods such as recurrent, convolutional, and Transformer models have been applied to precipitation prediction. …”
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Crypto market betas: the limits of predictability and hedging by Jan Sila, Michael Mark, Ladislav Kristoufek, Thomas A. Weber

“…Abstract This article analyzes the predictability of market betas concerning cryptocurrency assets and evaluates the efficiency of beta-hedged, market-neutral portfolios. …”
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Prediction of Drifter Trajectory Using Evolutionary Computation by Yong-Wook Nam, Yong-Hyuk Kim

“…As the evaluation measure, a method that gives a better score as the Mean Absolute Error (MAE) when the difference between the predicted position in time and the actual position is lower and the Normalized Cumulative Lagrangian Separation (NCLS), which is widely used as a trajectory evaluation method of drifters, were used. …”
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DATA MINING APPROACH FOR PREDICTING STUDENT PERFORMANCE by Edin Osmanbegović, Mirza Suljić

“…Using data mining the aim was to develop a model which can derive the conclusion on students' academic success. Different methods and techniques of data mining were compared during the prediction of students' success, applying the data collected from the surveys conducted during the summer semester at the University of Tuzla, the Faculty of Economics, academic year 2010-2011, among first year students and the data taken during the enrollment. …”
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