Evaluation of a Computed Tomography-based Technique for Predicting Atrial Fibrillation Recurrence Following Ablation Using an Adjusted Skeletal Muscle Index by Pingchuan Ma, Zhicheng Gao, Jiaqi Bao, Yilan Hu, Pengfei Sun, Qiqi Yan, Lifang Ye, Lihong Wang

“…Methods: This study included 641 patients with AF who underwent radiofrequency ablation. …”
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Construction and evaluation of an anxiety risk prediction model for the elderly (老年人焦虑风险预测模型的构建与评价) by ZHANG Jie (张洁), ZHU Xuehua (祝雪花)

“…Objective To construct a risk prediction model and evaluating its predictive forecasting performance to predict the occurrence of anxiety in older adults. …”
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Construction and evaluation of a predictive model for the degree of coronary artery occlusion based on adaptive weighted multi-modal fusion of traditional Chinese and western medic... by Jiyu ZHANG, Jiatuo XU, Liping TU, Hongyuan FU

“…Objective: To develop a non-invasive predictive model for coronary artery stenosis severity based on adaptive multi-modal integration of traditional Chinese and western medicine data. …”
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Comparative Discrimination of Life’s Simple 7, Life’s Essential 8, and Life’s Crucial 9: Evaluating the impact of added complexity on mortality prediction by Xu Zhu, Iokfai Cheang, Yiyang Fu, Sitong Chen, Gengmin Liang, Huaxin Yuan, Ling Zhu, Haifeng Zhang, Xinli Li

“…This study aimed to assess whether the additional components in LE8 and LC9 enhance mortality prediction compared to LS7. Methods Data from 22,382 participants in the NHANES 2005–2018 were analyzed. …”
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Myeloid response evaluated by noninvasive CT imaging predicts post-surgical survival and immune checkpoint therapy benefits in patients with hepatocellular carcinoma by Kangqiang Peng, Xiao Zhang, Xiao Zhang, Zhongliang Li, Yongchun Wang, Yongchun Wang, Hong-Wei Sun, Wei Zhao, Wei Zhao, Jielin Pan, Jielin Pan, Xiao-Yang Zhang, Xiaoling Wu, Xiangrong Yu, Xiangrong Yu, Chong Wu, Yulan Weng, Xiaowen Lin, Dingjie Liu, Dingjie Liu, Meixiao Zhan, Meixiao Zhan, Jing Xu, Limin Zheng, Limin Zheng, Yaojun Zhang, Ligong Lu, Ligong Lu

“…The prognostic performance of the Clinical-iMRS nomogram was better than that of a single parameter (p < 0.05), with a 1-, 3-, and 5-year C-index for RFS of 0.729, 0.709, and 0.713 in the training, test, and surgical resection cohorts, respectively. A high iMRS score predicted a higher proportion of objective response (vs. progressive disease or stable disease; odds ratio, 2.311; 95% CI, 1.144–4.672; p = 0.020; AUC, 0.718) in patients treated with anti-PD-1 and PD-L1.ConclusionsiMRS may provide a promising method for predicting local myeloid immune responses in HCC patients, inferring postsurgical prognosis, and evaluating benefits of immune checkpoint therapy.…”
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Collecting Performance Prediction for the Rubber Collector in Horizontal Wellbore Based on AutoML by Shaohua Li, Yang Li, Longlin Chen, Xianbin Wang, Weihang Kong

“…To objectively study the influencing factors of rubber collector performance in horizontal wellbores and identify parameter optimization directions, this paper introduces a modeling approach for rubber collectors in horizontal wellbores and develops a corresponding performance prediction method using AutoML(implemented with AutoGluon V1.2.0). …”
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USING THE METHODS OF SYSTEMS BIOLOGY FOR PREDICTING PERSPECTIVE TARGET GENES TO SELECT C3 AND C4 CEREALS FOR OXIDATIVE STRESS RESISTANCE by A. V. Doroshkov, A. V. Bobrovskikh

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Comparison of methods for tuning machine learning model hyper-parameters: with application to predicting high-need high-cost health care users by Christopher Meaney, Xuesong Wang, Jun Guan, Therese A. Stukel

“…Objective To compare several (nine) hyper-parameter optimization (HPO) methods, for tuning the hyper-parameters of an extreme gradient boosting model, with application to predicting high-need high-cost health care users. …”
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Impact of harmonization and oversampling methods on radiomics analysis of multi-center imbalanced datasets: application to PET-based prediction of lung cancer subtypes by Dongyang Du, Isaac Shiri, Fereshteh Yousefirizi, Mohammad R. Salmanpour, Jieqin Lv, Huiqin Wu, Wentao Zhu, Habib Zaidi, Lijun Lu, Arman Rahmim

“…Abstract Background Medical imaging data frequently encounter image-generation heterogeneity and class imbalance properties, challenging strong generalized predictive performances with data-driven machine-learning methods. …”
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Experimental Investigation of Land Mobile Prediction Methods and Modeling of Radio Planning Tool Parameters along Indian Rail Road Rural Zones by M. V. S. N. Prasad, P. K. Dalela, M. Chaitanya

“…In order to identify the suitable prediction methods for Indian rail road rural zones, train-based measurements were conducted in the northern and western rural zones along rail roads. …”
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A comparative study of regression methods to predict forest structure and canopy fuel variables from LiDAR full-waveform data by P. Crespo-Peremarch, L.A. Ruiz, A. Balaguer-Beser

“…Regression methods are widely employed in forestry to predict and map structure and canopy fuel variables. …”
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Determination of the sodium retardation factor using different methods: Analysis of their characteristics and impact on the solute movement prediction in a structured Brazilian soi... by Vanessa Godoy, Gian Franco Napa-García, Lázaro Zuquette

“…Its value can vary significantly depending on the method used for its determination. In this paper, the sodium Rd is experimentally determined using undisturbed sandy columns to compare four methods of Rd determination and assess the impact of the chosen method in the prediction of sodium movement. …”
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Prediction of key toxicity endpoints of AP-238 a new psychoactive substance for clinical toxicology and forensic purposes using in silico methods by Kamil Jurowski, Alicja Krośniak

“…A quantitative assessment of AP-238’s acute toxicity (AT) was performed by predicting theoretical LD50 values for both rats and mice across different administration routes using various in silico methods. …”
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Feature-based ensemble modeling for addressing diabetes data imbalance using the SMOTE, RUS, and random forest methods: a prediction study by Younseo Jang

“…Purpose This study developed and evaluated a feature-based ensemble model integrating the synthetic minority oversampling technique (SMOTE) and random undersampling (RUS) methods with a random forest approach to address class imbalance in machine learning for early diabetes detection, aiming to improve predictive performance. …”
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Prediction of the Calorific Value and Moisture Content of <i>Caragana korshinskii</i> Fuel Using Hyperspectral Imaging Technology and Various Stoichiometric Methods by Xuehong De, Haoming Li, Jianchao Zhang, Nanding Li, Huimeng Wan, Yanhua Ma

“…Finally, a comprehensive comparison of the modeling effectiveness of all methods was carried out, and the SNV-IRIV-PLSR modeling combination was the best for water content prediction, with its prediction set determination coefficient <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mo>(</mo><msubsup><mrow><mi>R</mi></mrow><mrow><mi>P</mi></mrow><mrow><mn>2</mn></mrow></msubsup><mo>)</mo></mrow></semantics></math></inline-formula>, root mean square error of prediction (RMSEP), and relative percentage deviation (RPD) of 0.9693, 0.2358, and 5.6792, respectively. …”
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Evaluating the Predictive Potential of Neutrophil-to-Lymphocyte and Platelet-to-Lymphocyte Ratios in the Second-Trimester Preterm Premature Rupture of Membranes (PPROM): A Retrospe... by Pınar Tuğçe Özer, Mehmet Özer, Süleyman Cemil Oğlak, Adnan Budak, Abdurrahman Hamdi İnan, Gökhan Tosun, Emrah Töz, Emine Zeynep Yılmaz, Varol Gülseren, Gökhan Bolluk, Mehmet Özeren

“…Methods: This retrospective case-control study was performed from June 2018 to June 2023. …”
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Multicenter evaluation of predictive clinical and imaging factors for pathological response in non-small cell lung cancer patients treated with neoadjuvant chemotherapy and immune... by Mengzhe Zhang, Meng Yan, Zekun Li, Shuai Jiang, Zuo Liu, Pengpeng Zhang, Zhenfa Zhang

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Research on deep learning-based fracture network inversion method for shale gas reservoirs by CHEN Weiming, JIANG Lin, LUO Tongtong, LI Yue, WANG Jianhua

“…Numerical simulation prediction models require a large number of engineering geological parameters, leading to poor prediction effects for geological data that are incomplete or missing well sections. …”
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An Improved Decline Curve Analysis Method via Ensemble Learning for Shale Gas Reservoirs by Yu Zhou, Zaixun Gu, Changyu He, Junwen Yang, Jian Xiong

“…By combining the strengths of individual models, it offers a more robust and accurate prediction framework. We evaluated this method using data from 22 shale gas wells in region L, China, comparing it to six traditional DCA models, including Arps and the Logistic Growth Model (LGM). …”
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An Evaluation of Draminski Detector as an Early Detection Tool for Subclinical Mastitis in Dairy Cattle in Pondok Ranggon Farm by Herwin Pisestyani, Indra Permana, Chaerul Basri, Denny Widaya Lukman, Mirnawati Sudarwanto

“…This test method was also considered to have poor test performance in diagnosing cases of subclinical mastitis in dairy cattle with a sensitivity value of 3,5%, specificity 100%, positive predictive value 100%, negative predictive value 18,8%, estimated prevalence 2,9%, and the true prevalence was 81,7%. …”
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