Development of a high-performing, cost-effective and inclusive Afrocentric predictive model for stroke: a meta-analysis approach  by M Nweke, P Oyirinnaya, P Nwoha, SB Mitha, N Mshunqane, N Govender, M Ukwuoma, SC Ibeneme

“…Conclusions Targeted screening via the CAPMS 1 and CAPMS 2 models offers a cost-effective solution for stroke screening in African clinics and communities. …”
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Predicting the risk of postoperative avascular necrosis in patients with talar fractures based on an interpretable machine learning model by Jian Zhang, Jian Zhang, Jian Zhang, Jihai Xu, Jihai Xu, Jiapei Yu, Jiapei Yu, Jiapei Yu, Hong Chen, Hong Chen, Xin Hong, Songou Zhang, Xin Wang, Xin Wang, Chengchun Shen, Chengchun Shen, Chengchun Shen

“…Six machine learning algorithms were employed to construct the prediction models. …”
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Machine learning-driven prediction model for cuproptosis-related genes in spinal cord injury: construction and experimental validation by Yimin Zhou, Xin Li, Zixiu Wang, Liqi Ng, Rong He, Chaozong Liu, Gang Liu, Xiao Fan, Xiaohong Mu, Yu Zhou, Yu Zhou

“…Three machine learning models (RF, LASSO, and SVM) were constructed to screen candidate genes, and a Nomogram model was used for verification. …”
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NMD-FusionNet: a multimodal fusion-based medical imaging-assisted diagnostic model for liver cancer by Qing Ye, Minghao Luo, Jing Zhou, Chunlei Cheng, Lin Peng, Jia Wu

“…The framework includes a three-stage pipeline: first, a refined non-local means filtering algorithm is employed for pre-screening, discarding over 80% of non-diagnostic images using adaptive thresholding; second, a multimodal image fusion method integrates multi-phase, multi-source liver cancer image data through multi-scale decomposition and precise fusion rules to reduce noise and motion artifacts; third, a dual-path DconnNet segmentation network is constructed, incorporating a directional excitation module in the encoder and a spatial awareness unit in the decoder, guided by a boundary-constrained loss function to enhance segmentation accuracy. …”
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Toward Adaptive and Immune-Inspired Viable Supply Chains: A PRISMA Systematic Review of Mathematical Modeling Trends by Andrés Polo, Daniel Morillo-Torres, John Willmer Escobar

“…At the methodological level, a high degree of diversity in modeling techniques was observed, with an emphasis on mixed-integer linear programming (MILP), robust optimization, multi-objective modeling, and the increasing use of bio-inspired algorithms, artificial intelligence, and simulation frameworks. …”
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Risk Factors and Predictive Model for Ischemic Complications in Endovascular Treatment of Intracranial Aneurysms: Insights From a Large Patient Cohort by Jianwen Jia, Zeping Jin, Mirzat Turhon, Yixin Lin, Xinjian Yang, Yang Wang, Yunpeng Liu

“…A total of five potential factors were screened using LASSO regression, XGBoost, and Randomforest algorithms: hypertension, history of alcohol consumption, multiple IAs, rupture status, and antiplatelet agent. …”
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Collaborative governance model for spoil disposal and gully infill land creation near open-pit coal mines by Shaogang LEI, Jianying ZHANG, Chang LIU, Liang WANG, Zhenwang JIA

“…The main technical steps include: extracting the location of the gully to be treated based on the algorithm of constructing concentric rectangular windows inside and outside, optimizing the earthwork allocation path of the waste dump based on the “source sink” theory, backfilling the gully area based on the reshaping of the near natural landform, screening the waste materials and reconstructing the soil layer profile of the gully backfilling, greening and land reuse of the covering soil, and evaluating the ecological effects of collaborative mining and treatment. …”
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Enhanced pre-recruitment framework for clinical trial questionnaires through the integration of large language models and knowledge graphs by Chen Zihang, Liu Liang, Su Qianmin, Cheng Gaoyi, Huang Jihan, Li Ying

“…However, recent years have seen the evolution of knowledge graphs and the introduction of large language models (LLMs), providing innovative approaches for the pre-screening and recruitment phases of clinical trials. …”
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DIPOLE ANTENNAS WITH A SECTOR-SHAPED RADIATION PATTERN by M. M. Gorobets, N. P. Yeliseyeva, S. L. Berdnyk, O. M. Horobets

“…Results. The algorithms and calculation programs developed allow studying the electrodynamic characteristics of the antenna over a wide range of screen electrical dimensions and distances between the dipole and the screen. …”
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Machine learning models in enhancing prediction of health-related indices among older adults: A scoping review by Raoof Nopour

“…Objective: This scoping review aims to investigate machine learning models in predicting health-related indices among older adults. …”
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Comparative evaluation of machine learning models for enhancing diagnostic accuracy of otitis media with effusion in children with adenoid hypertrophy by Xiaote Zhang, Qiaoyi Xie, Ganggang Wu

“…Given the urgent need for improved diagnostic methods and extensive characterization of risk factors for OME in AH children, developing diagnostic models represents an efficient strategy to enhance clinical identification accuracy in practice.ObjectiveThis study aims to develop and validate an optimal machine learning (ML)-based prediction model for OME in AH children by comparing multiple algorithmic approaches, integrating clinical indicators with acoustic measurements into a widely applicable diagnostic tool.MethodsA retrospective analysis was conducted on 847 pediatric patients with AH. …”
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A Computationally Efficient Model Predictive Control Energy Management Strategy for Hybrid Vehicles Considering Driving Style by Yalian Yang, Yuqi Chen, Changdong Liu

“…The driving-style adaptive Pontryagin’s minimum principle for model predictive control (DSA-PMP-MPC) algorithm was designed as a real-time energy management strategy for Hybrid Electric Vehicles (HEVs). …”
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Multi-Comparison of Different Ocular Imaging Modality-based Deep Learning Models for Visually Significant Cataract Detection by Jocelyn Hui Lin Goh, BEng, Xiaofeng Lei, MSc, Miao-Li Chee, MPH, Yiming Qian, PhD, Marco Yu, PhD, Tyler Hyungtaek Rim, MD, PhD, Simon Nusinovici, PhD, David Ziyou Chen, MBBS, FRCOphth, Kai Hui Koh, BSc, Samantha Min Er Yew, BSc, Yibing Chen, BEng, Victor Teck Chang Koh, MBBS, MMed, Charumathi Sabanayagam, MD, PhD, Tien Yin Wong, MD, PhD, Xinxing Xu, PhD, Rick Siow Mong Goh, PhD, Yong Liu, PhD, Ching-Yu Cheng, MD, PhD, Yih-Chung Tham, PhD

“…A community study data set of nonmydriatic retinal photos (N = 310 eyes) was used for external testing of the retinal model. Methods: We developed 3 single-modality DL models (retinal, slit beam, and diffuse anterior segment photos) and 4 ensemble models (4 different combinations of the 3 single-modality models) to detect visually significant cataract (VSC). …”
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An integrated machine learning framework for developing and validating diagnostic models and drug predictions based on ulcerative colitis genes by Na An, Zhongwen Lu, Yang Li, Bing Yang, Shaozhen Ji, Xu Dong, Zhaoliang Ding

“…To build a diagnostic model for UC, we applied 113 combinations of 12 machine learning algorithms. …”
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Artificial Intelligence–Aided Diagnosis System for the Detection and Classification of Private-Part Skin Diseases: Decision Analytical Modeling Study by Wei Wang, Xiang Chen, Licong Xu, Kai Huang, Shuang Zhao, Yong Wang

“…Compared with existing advanced algorithms, this system is more accurate in identifying PPSDs. …”
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Diabetes and Cataracts Development—Characteristics, Subtypes and Predictive Modeling Using Machine Learning in Romanian Patients: A Cross-Sectional Study by Adriana Ivanescu, Simona Popescu, Adina Braha, Bogdan Timar, Teodora Sorescu, Sandra Lazar, Romulus Timar, Laura Gaita

“…<i>Conclusions:</i> These findings suggest that diabetes may impact the type of cataract that develops, with CC being notably more prevalent in diabetic patients. This has important implications for screening and management strategies for cataract formation in diabetic populations.…”
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Predicting postoperative malnutrition in patients with oral cancer: development of an XGBoost model with SHAP analysis and web-based application by Lixia Kuang, Lixia Kuang, Jingya Yu, Yunyu Zhou, Yu Zhang, Yu Zhang, Guangman Wang, Guangman Wang, Fangmin Zhang, Grace Paka Lubamba, Grace Paka Lubamba, Xiaoqin Bi, Xiaoqin Bi

“…The dataset was divided into a training set (70%) and a validation set (30%). Predictive models were developed via four supervised machine learning algorithms: logistic regression (LR), support vector machine (SVM), light gradient boosting machine (LGBM), and extreme gradient boosting (XGBoost). …”
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Investigation of the influence of the heterogeneous structure of concrete on its strength by V.M. Volchuk, M.A. Kotov, Ye G. Plakhtii, O.A. Tymoshenko, O.H. Zinkevych

“…This approach allowed screening out low-sensitivity structure features from the multifractal spectrum. …”
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Developing an Uncrewed Aerial Vehicle (UAV)-Based Prediction Model for the Rice Harvest Index Using Machine Learning by Zhaoyang Pan, Zhanhua Lu, Liting Zhang, Wei Liu, Xiaofei Wang, Shiguang Wang, Hao Chen, Haoxiang Wu, Weicheng Xu, Youqiang Fu, Xiuying He

“…Based on the above characteristics, this study used a variety of machine learning algorithms to construct a harvest index prediction model. …”
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Machine learning-based prediction model for lung ischemia-reperfusion injury: insights from disulfidptosis-related genes by Yanpeng Zhang, Yanpeng Zhang, Yanpeng Zhang, Jingyang Sun, Jingyang Sun, Jingyang Sun, Yihan Lin, Yihan Lin, Yihan Lin, Rongxuan Jiang, Rongxuan Jiang, Rongxuan Jiang, Niuniu Dong, Niuniu Dong, Niuniu Dong, Huanhuan Dong, Huanhuan Dong, Huanhuan Dong, Peng Li, Peng Li, Peng Li, Jinteng Feng, Jinteng Feng, Jinteng Feng, Zijiang Zhu, Guangjian Zhang, Guangjian Zhang, Guangjian Zhang

“…Multiple machine learning algorithms (Generalized Linear Model, Support Vector Machine, and Random Forest) were applied for joint screening, leading to the construction of a predictive model. …”
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