Predicting outcomes of expectant and medical management in early pregnancy miscarriage using machine learning to develop and validate multivariable clinical prediction models by Sughashini Murugesu, Kristofer Linton-Reid, Emily Braun, Jennifer Barcroft, Nina Cooper, Margaret Pikovsky, Alex Novak, Nina Parker, Catriona Stalder, Maya Al-Memar, Srdjan Saso, Eric O. Aboagye, Tom Bourne

“…A combination of eight linear, Bayesian, neural-net and tree-based machine learning algorithms were applied to ten different feature sets. …”
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From Narratives to Diagnosis: A Machine Learning Framework for Classifying Sleep Disorders in Aging Populations: The <i>sleepCare</i> Platform by Christos A. Frantzidis

“…Next, a Support Vector Machine (SVM) was trained on GloVe-based word embeddings to capture semantic context. …”
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EcoTaskSched: a hybrid machine learning approach for energy-efficient task scheduling in IoT-based fog-cloud environments by Asfandyar Khan, Faizan Ullah, Dilawar Shah, Muhammad Haris Khan, Shujaat Ali, Muhammad Tahir

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Hypothermic machine perfusion of a donor kidney using an experimental dextran-40-based preservation solution and orthotopic transplantation (experimental study) by V. G. Shestakova, V. K. Bogdanov, R. D. Pavlov, V. M. Terekhov, A. S. Timanovsky, A. A. Zharikov, A. N. Shibaev, N. V. Grudinin

“…Objective: to evaluate the efficacy of hypothermic machine perfusion (HMP) of a donor kidney obtained from a non-heartbeating (NHB) donor, using an experimental dextran-40-based preservation solution, in subsequent orthotopic transplantation in a rabbit model.Materials and methods. …”
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Machine learning prediction models for mortality risk in sepsis-associated acute kidney injury: evaluating early versus late CRRT initiation by Chuanren Zhuang, Ruomeng Hu, Ke Li, Zhengshuang Liu, Songjie Bai, Sheng Zhang, Xuehuan Wen

“…Subgroup analyses stratified patients by disease severity using SOFA scores (low ≤10, medium 11–15, high &gt;15) and creatinine levels (low ≤3 mg/dL, medium 3–5 mg/dL, high &gt;5 mg/dL). Multiple machine learning models were developed and evaluated to predict patient prognosis, with Shapley Additive exPlanations (SHAP) analysis identifying key prognostic factors.ResultsAfter propensity score matching, late CRRT initiation was associated with improved survival probability, but led to increased hospital and ICU stays. …”
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Interpretable machine learning for depression recognition with spatiotemporal gait features among older adults: a cross-sectional study in Xiamen, China by Shaowu Lin, Sicheng Li, Ya Fang

“…Gait data were recorded using a Microsoft Kinect in the indoor experimental area. χ2 and t-tests were used for statistical comparisons. Four machine learning techniques including Logistic Regression, Support Vector Machine, Gradient Boosting Decision Tree, and Random Forest were employed to develop predictive models for depression. …”
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Factors influencing the response to periodontal therapy in patients with diabetes: post hoc analysis of a randomized clinical trial using machine learning by Nidia CASTRO DOS SANTOS, Arthur MANGUSSI, Tiago RIBEIRO, Rafael Nascimento de Brito SILVA, Mauro Pedrine SANTAMARIA, Magda FERES, Thomas VAN DYKE, Ana Carolina LORENA

“…Abstract Objective To evaluate factors influencing the response to periodontal therapy in patients with periodontitis and type 2 diabetes mellitus (DM) using machine learning (ML) techniques, considering periodontal parameters, metabolic status, and demographic characteristics. …”
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Prediction of in-hospital mortality of Clostriodiodes difficile infection using critical care database: a big data-driven, machine learning approach by Mengru Liu, Mengling Feng, Kewin Tien Ho Siah, Wesley Yeung, Hao Du, Valencia Zhang Ru-Yan, Readon Teh, Christopher Yu En Tan, Christina Scaduto, Sarah Bolongaita, Maria Teresa Kasunuran Cruz, Xiaohao Lin, Yan Yuan Tan

“…We subsequently trained three machine learning models: logistic regression (LR), random forest (RF) and gradient boosting machine (GBM) to predict in-hospital mortality. …”
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Analyzing Key Predictors of Postoperative Delirium Following Coronary Artery Bypass Grafting and Aortic Valve Replacement: A Machine Learning Perspective by Marija Stošić, Velimir Perić, Dragan Milić, Milan Lazarević, Jelena Živadinović, Vladimir Stojiljković, Aleksandar Kamenov, Aleksandar Nikolić, Mlađan Golubović

“…SHAP analysis identified sedation, mechanical ventilation, and their interactions with fibrinogen, troponin I, leukocyte parameters, and lung infection as key predictors. <i>Conclusions</i>: This study demonstrates that an interpretable machine learning approach can enhance POD prediction, providing insights into the combined impact of multiple clinical, biochemical, and perioperative factors. …”
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Winter Oilseed Rape LAI Inversion via Multi-Source UAV Fusion: A Three-Dimensional Texture and Machine Learning Approach by Zijun Tang, Junsheng Lu, Ahmed Elsayed Abdelghany, Penghai Su, Ming Jin, Siqi Li, Tao Sun, Youzhen Xiang, Zhijun Li, Fucang Zhang

“…These variables were then partitioned into distinct combinations and input into three machine learning models—Support Vector Machine (SVM), Backpropagation Neural Network (BPNN), and Extreme Gradient Boosting (XGBoost)—to estimate winter oilseed rape LAI. …”
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Application of supervised machine learning and unsupervised data compression models for pore pressure prediction employing drilling, petrophysical, and well log data by Abu Bakker Siddique, Tanveer Alam Munshi, Nazmul Islam Rakin, Mahamudul Hashan, Sushmita Sarker Chnapa, Labiba Nusrat Jahan

“…These recordings encompass depth data; well logs, including NPHI, GR, DT, RD, RHOB, RS, and RT; drilling activities, specifically ROP; and petrophysical parameters, including BVW, K, PHIF, SW, and VCL. Pore pressure is used as the output level to generate data-driven models. 70% of the dataset is used for training the machine learning models, while the remaining 30% is reserved for testing the models to evaluate their performance and generalization capability. …”
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Unveiling lipoprotein subfractions signature in high-FNPO PCOS: implications for PCOM diagnosis and risk assessment using advanced machine learning models by Xueqi Yan, Ziyi Yang, Hui Zhao, Gengchen Feng, Shumin Li, Yimeng Li, Yu Sun, Jinlong Ma, Han Zhao, Xueying Gao, Shigang Zhao

“…Results High-FNPO PCOS cases presented worse lipid parameters compared with low-FNPO cases and controls. …”
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Effect of cutting parameters and tool coating on residual stress and cutting temperature in dry hard turning of AISI 52100 steel using finite element method by Sandip Mane, Rajkumar Bhimgonda Patil, Md Irfanul Haque Siddiqui, Choon Kit Chan, Yong Xu

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Klasifikasi Citra Sampah Menggunakan Support Vector Machine dengan Ekstraksi Fitur Gray Level Co-Occurrence Matrix dan Color Moments by Iffa Zainan Nisa, Sukmawati Nur Endah, Priyo Sidik Sasongko, Retno Kusumaningrum, Khadijah Khadijah, Rismiyati Rismiyati

“…Dataset TrashNet digunakan untuk mengevaluasi metode yang diusulkan. Beberapa parameter penting yang digunakan dalam penelitian ini adalah orientasi sudut GLCM, parameter C (soft margin) pada SVM, dan parameter ???? …”
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Use of X means and C4.5 algorithms on lateral cephalometric measurements to identify craniofacial patterns by Merve Gonca, Mehmet Birol Özel

“…The interincisal angle was the main parameter determining the distinction between Clusters 0 and 1. …”
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A Learned Reduced-Rank Sharpening Method for Multiresolution Satellite Imagery by Sveinn E. Armannsson, Magnus O. Ulfarsson, Jakob Sigurdsson

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Klasifikasi Ulasan Palsu Menggunakan Borderline Over Sampling (BOS) dan Support Vector Machine (SVM) (Studi Kasus : Ulasan Tempat Makan) by Aisyah Awalina, Fitra Abdurrachman Bachtiar, Indriati Indriati

“…Ulasan palsu bisa secara efektif dibedakan menggunakan machine learning. Namun, banyak dari dataset ulasan palsu ini tidak seimbang (imbalanced dataset) sehingga dapat mempengaruhi hasil klasifikasi. …”
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Comparison of Support Vector Machine (SVM) and Random Forest (RF) Algorithm Performance with Random Undersampling Technique to Predict Gestational Diabetes Mellitus Risk by Annisa Damayanti, Anna Baita

“…The best parameter search process is carried out using GridSearchCV on both models. …”
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Cutting Force Prediction of Ti6Al4V using a Machine Learning Model of SPH Orthogonal Cutting Process Simulations by Hagen Klippel, Eduardo Gonzalez Sanchez, Margolis Isabel, Matthias Röthlin, Mohamadreza Afrasiabi, Kuffa Michal, Konrad Wegener

“…The prediction of machining processes is a challenging task and usually requires a large experimental basis. …”
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Study on Machinability Behaviour and Simultaneous Optimisation of Multiple Responses Using Taguchi-Based Grey Relational Analysis in End Milling of Aluminum Hybrid Composites by Kumar S., Dhanesh G. Mohan

“…The present work reveals the machinability characteristics of Al7068/Si3N4/BN hybrid composite materials. …”
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