Interpretable machine learning for predicting isolated basal septal hypertrophy. by Lei Gao, Boyan Tian, Qiqi Jia, Xingyu He, Guannan Zhao, Yueheng Wang

“…The data were divided into training and test sets in a 7:3 ratio. Five machine learning algorithms -XGBoost, Random Forest(RF), Dicision tree(DT), K-Nearest Neighbor classification(KNN), and Naive Bayes(NB) were applied to construct the models, combined with logistic regression (LR) based on Lasso regression. …”
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Cerebrospinal Fluid Leakage Combined with Blood Biomarkers Predicts Poor Wound Healing After Posterior Lumbar Spinal Fusion: A Machine Learning Analysis by Pang Z, Ou Y, Liang J, Huang S, Chen J, Huang S, Wei Q, Liu Y, Qin H, Chen Y

“…The data was divided into test and validation groups in a 7:3 ratio. In the test group, logistic regression analysis, support vector machine (SVM), random forest (RF), decision tree (DT), XGboost, Naïve Bayes (NB), k-Nearest Neighbor (KNN), and Multi-Layer Perceptron (MLP) were used to identify specific variables. …”
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An Explainable Machine Learning Approach for IoT-Supported Shaft Power Estimation and Performance Analysis for Marine Vessels by Yiannis Kiouvrekis, Katerina Gkirtzou, Sotiris Zikas, Dimitris Kalatzis, Theodor Panagiotakopoulos, Zoran Lajic, Dimitris Papathanasiou, Ioannis Filippopoulos

“…A diverse set of models—ranging from traditional algorithms such as Decision Trees and Support Vector Machines to advanced ensemble methods like XGBoost and LightGBM—were developed and evaluated. …”
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Early-life management practices and their association with dairy herd longevity, productivity, and profitability by Gabriel M. Dallago, Daniel Warner, Elsa Vasseur

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The Role of Immunohistochemistry as a Surrogate Marker in Molecular Subtyping and Classification of Bladder Cancer by Tatiana Cano Barbadilla, Martina Álvarez Pérez, Juan Daniel Prieto Cuadra, Mª Teresa Dawid de Vera, Fernando Alberca-del Arco, Isabel García Muñoz, Rocío Santos-Pérez de la Blanca, Bernardo Herrera-Imbroda, Elisa Matas-Rico, Mª Isabel Hierro Martín

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Elevating E-Service Standards in Bangladesh: Merging FAHP and TOPSIS for Strategic Online Business Solutions by Farzana Tasnim, Tanjim Mahmud, Shefayatuj Johara Chowdhury, Tasnia Fahrin Choity, Aisha Ahmed, Mohammad Saeed Hasan Chowdhury, Mohammad Shahadat Hossain, Karl Andersson

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Machine learning for prediction of 30-day mortality in patients with advanced cancer: comparing pan-cancer and single-cancer models by S. Bjerregaard-Michelsen, L.Ø. Poulsen, A. Bjerrum, M. Bøgsted, C. Vesteghem

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Evaluating the value of machine learning models for predicting hematoma expansion in acute spontaneous intracerebral hemorrhage based on CT imaging features of hematomas and surrou... by Tianyu Yang, Tianyu Yang, Zhen Zhao, Yan Gu, Shengkai Yang, Yonggang Zhang, Lei Li, Ting Wang, Zhongchang Miao

“…Its robust performance supports its utility in emergency settings to guide clinical decision-making effectively.…”
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Hyperspectral Inversion of Soil Cu Content in Agricultural Land Based on Continuous Wavelet Transform and Stacking Ensemble Learning by Kai Yang, Fan Wu, Hongxu Guo, Dongbin Chen, Yirong Deng, Zaoquan Huang, Cunliang Han, Zhiliang Chen, Rongbo Xiao, Pengcheng Chen

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Right-to-left-shunts in patients scheduled for neurosurgical intervention in semi-sitting position – a literature review based on two case scenarios by Marina Nikolic, C. Eisner, J. O. Neumann, D. Haux, S. M. Krieg, M. O. Wielpütz, M. A. Weigand, U. Tochtermann, Dania Fischer

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Machine learning-based prediction of antimicrobial resistance and identification of AMR-related SNPs in Mycobacterium tuberculosis by Yi Xu, Ying Mao, Xiaoting Hua, Yan Jiang, Yi Zou, Zhichao Wang, Zubi Liu, Hongrui Zhang, Lingling Lu, Yunsong Yu

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Free-space terabit/s coherent optical links via platicon frequency microcombs by Wenting Wang, Hao Liu, Jiagui Wu, James F. McMillan, Dong IL Lee, Futai Hu, Wenzheng Liu, Jinghui Yang, Hangbo Yang, Abhinav Kumar Vinod, Yahya H. Ezzeldin, Christina Fragouli, Mingbin Yu, Patrick Guo-Qiang Lo, Dim-Lee Kwong, Devin S. Kahrs, Ninghua Zhu, Chee Wei Wong

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In Vitro Oral Cavity Permeability Assessment to Enable Simulation of Drug Absorption by Pankaj Dwivedi, Priyata Kalra, Haiying Zhou, Khondoker Alam, Eleftheria Tsakalozou, Manar Al-Ghabeish, Megan Kelchen, Giovanni M. Pauletti

“…<b>Conclusions</b>: Experimental permeation data collected for selected APIs in FDA-approved oral cavity products will serve as a training set to aid the development of predictive computational models for improving algorithms that describe drug absorption from the oral cavity. …”
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Urban–rural disparities in fall risk among older Chinese adults: insights from machine learning-based predictive models by LiHan Lin, LiHan Lin, XiaoYang Liu, CaiHua Cai, YiKun Zheng, Delong Li, GuoPeng Hu

“…Predictive models for fall risk over the next 3 years among urban and rural older populations were developed using five machine learning algorithms. …”
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THE EXPERIENCE OF THE AUTOMATION OF THE STATISTICAL REPORTING FORMATION ON WAGES IN MULTIDISCIPLINARY MEDICAL INSTITUTION by O. E. Karpov, D. N. Nikitenko, O. Ya. Gembara, T. A. Manakova

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Bi-modal ultrasound radiomics and habitat analysis enhanced the pre-operative prediction of axillary lymph node burden in patients with early-stage breast cancer by Jing Xu, Pan Qi, Xiaoyan Ou, Qiaoxin Zhong, Zhen-Wen Chen, Yin Wang, Aijiao Yi, Bin Wang

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Comparative assessment of line probe assays and targeted next-generation sequencing in drug-resistant tuberculosis diagnosisResearch in context by Giovanna Carpi, Marva Seifert, Andres De la Rossa, Swapna Uplekar, Camilla Rodrigues, Nestani Tukvadze, Shaheed V. Omar, Anita Suresh, Timothy C. Rodwell, Rebecca E. Colman

“…For fluoroquinolones (moxifloxacin and levofloxacin), the MTBDRsl LPA and ONT had similar sensitivities (94.3% and 92.7%, and 94.8% and 93.9%, respectively), while GenoScreen outperformed both (97.3% and 96.6%). …”
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Explainable Boosting Machines Identify Key Metabolomic Biomarkers in Rheumatoid Arthritis by Fatma Hilal Yagin, Cemil Colak, Abdulmohsen Algarni, Ali Algarni, Fahaid Al-Hashem, Luca Paolo Ardigò

“…EBM, LightGBM, and AdaBoost algorithms were applied to generate a discriminatory model between RA and controls. …”
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Optimization of guidelines for Risk Of Recurrence/Prosigna testing using a machine learning model: a Swedish multicenter study by Una Kjällquist, Nikos Tsiknakis, Balazs Acs, Sara Margolin, Luisa Edman Kessler, Scarlett Levy, Maria Ekholm, Christine Lundgren, Erik Olsson, Henrik Lindman, Antonios Valachis, Johan Hartman, Theodoros Foukakis, Alexios Matikas

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Diagnostic Value of Glycosylated Extracellular Vesicle microRNAs in Gastric Cancer by Wang S, Ma C, Ren Z, Zhang Y, Hao K, Liu C, Xu L, He S, Zhang J

“…The signatures were screened in a discovery cohort of GC patients (n=55) and non-disease controls (n=46) using an integrated process, including high-throughput sequencing technology, screening using a complete bioinformatics algorithm, validation using RT-qPCR, and evaluation by constructing a diagnostic model. …”
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