Prediction of Insulin Resistance in Nondiabetic Population Using LightGBM and Cohort Validation of Its Clinical Value: Cross-Sectional and Retrospective Cohort Study by Ting Peng, Rujia Miao, Hao Xiong, Yanhui Lin, Duzhen Fan, Jiayi Ren, Jiangang Wang, Yuan Li, Jianwen Chen

“…A total of 5 machine learning algorithms, namely random forest, Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting, Gradient Boosting Machine, and CatBoost were used. …”
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Prediction of birthweight with early and mid-pregnancy antenatal markers utilising machine learning and explainable artificial intelligence by Manohar Pavanya, Krishnaraj Chadaga, Vennila J, Akhila Vasudeva, Bhamini Krishna Rao, Srikanth Prabhu, Shashikala K Bhat

“…We developed a stacked ensemble model that integrated various algorithms, including a custom-stacked ensemble approach and three XAI methodologies: Shapley Additive Explanations (SHAP), Local Interpretable Model-agnostic Explanations (LIME), and Anchor. …”
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AI in Medical Questionnaires: Innovations, Diagnosis, and Implications by Xuexing Luo, Yiyuan Li, Jing Xu, Zhong Zheng, Fangtian Ying, Guanghui Huang

“…Overall, 24 AI technologies were identified, covering traditional algorithms such as random forest, support vector machine, and k-nearest neighbor, as well as deep learning models such as convolutional neural networks, Bidirectional Encoder Representations From Transformers, and ChatGPT. …”
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Advancing personalized, predictive, and preventive medicine in bladder cancer: a multi-omics and machine learning approach for novel prognostic modeling, immune profiling, and ther... by Han Yan, Xinyu Ji, Bohan Li

“…Weighted gene co-expression network analysis (WGCNA) identified ICD-related modules, and machine learning algorithms (Lasso, Ridge, CoxBoost) were employed to construct the ICDRS. …”
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Syn-MolOpt: a synthesis planning-driven molecular optimization method using data-derived functional reaction templates by Xiaodan Yin, Xiaorui Wang, Zhenxing Wu, Qin Li, Yu Kang, Yafeng Deng, Pei Luo, Huanxiang Liu, Guqin Shi, Zheng Wang, Xiaojun Yao, Chang-Yu Hsieh, Tingjun Hou

“…Although many deep-learning-based molecular optimization algorithms have been proposed and may perform well on benchmarks, they usually do not pay sufficient attention to the synthesizability of molecules, resulting in optimized compounds difficult to be synthesized. …”
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Trajectory of breastfeeding among Chinese women and risk prediction models based on machine learning: a cohort study by Yi Liu, Jie Xiang, Ping Yan, Yuanqiong Liu, Peng Chen, Yujia Song, Jianhua Ren

“…Methods This study conducted a three-wave prospective cohort analysis to examine maternal breastfeeding trajectories within the first six months postpartum and to develop risk prediction models for each period using advanced machine learning algorithms. Conducted at a leading Maternal and Children's hospital in China from October 2021 to June 2022, data were gathered via self-administered surveys and electronic health records. …”
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Explainable machine learning models for mortality prediction in patients with sepsis in tertiary care hospital ICU in low- to middle-income countries by Saumya Diwan, Vinay Gandhi, Esha Baidya Kayal, Puneet Khanna, Amit Mehndiratta

“…Four machine learning algorithms, Random Forest, XGBoost, Extra Trees and Gradient Boosting classifiers were trained for the binary classification task (discharge from ICU and death in ICU) with the selected influential feature set. …”
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Picometre-level surface control of a closed-loop, adaptive X-ray mirror with integrated real-time interferometric feedback by Ioana-Theodora Nistea, Simon G. Alcock, Andrew Foster, Vivek Badami, Riccardo Signorato, Matteo Fusco

“…Aside from demonstrating the extreme sensitivity of the interferometer sensors, this study also highlights the voltage repeatability and stability of the programmable high-voltage power supply, the accuracy of the correction-calculation algorithms and the almost instantaneous response of the bimorph mirror to command voltage pulses. …”
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Retinal vein occlusion risk prediction without fundus examination using a no-code machine learning tool for tabular data: a nationwide cross-sectional study from South Korea by Na Hyeon Yu, Daeun Shin, Ik Hee Ryu, Tae Keun Yoo, Kyungmin Koh

“…Various machine learning algorithms were trained by incorporating all features from the health check-up data in the development set. …”
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Lateral shear interferometry for shape accuracy measurements of 3D-printed micro-optics by Zhao Yanqiu, Wang Lunwei, Siegle Leander, Giessen Harald

“…This advantage facilitates the development of a notably compact set-up, making lateral shear interferometry particularly advantageous for integration with microscope and 3D-printer, despite the requirement for more complex algorithms in analyzing lateral shear interferograms. …”
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Sensitive Multispectral Variable Screening Method and Yield Prediction Models for Sugarcane Based on Gray Relational Analysis and Correlation Analysis by Shimin Zhang, Huojuan Qin, Xiuhua Li, Muqing Zhang, Wei Yao, Xuegang Lyu, Hongtao Jiang

“…Subsequently, three supervised learning algorithms—Gradient Boosting Decision Tree (GBDT), Random Forest (RF), and Support Vector Machine (SVM)—were employed to develop both single-stage and multi-stage yield prediction models. …”
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A novel device-free Wi-Fi indoor localization using a convolutional neural network based on residual attention by Mashael Maashi, Alanoud Al Mazroa, Shoayee Dlaim Alotaibi, Asma Alshuhail, Muhammad Kashif Saeed, Ahmed S. Salama

“…However, existing convolutional neural network (CNN) fingerprinting placement algorithms have a limited receptive area, limiting their effectiveness since important data in CSI has not been thoroughly explored. …”
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Optimizing spatial normalization of multisubject inner ear MRI: comparison of different geometry-preserving co-registration approaches by Johannes Gerb, Valerie Kirsch, Emilie Kierig, Thomas Brandt, Marianne Dieterich, Rainer Boegle

“…The semi-manual method used a three-point landmark-based approach (3P), two automatic methods were based on unassisted standard algorithms (Advanced Normalization Tools (ANTs), Elastix (EL)), while the fourth automatic method utilized a volumetrically dilated, atlas-based mask (thick inner ear, TIE) for probabilistic inner ear masking. …”
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Real‐Climatic Microcontroller‐in‐the‐Loop (RCMIL) Framework: A Novel, Rapid, and Cost‐Effective Approach for Verifying Photovoltaic Control Systems by Ambe Harrison, Idriss Dagal, Wulfran Fendzi Mbasso, Fritz Nguemo Kemdoum, Reagan Jean Jacques Molu, Hamid Belghiti, Pradeep Jangir

“…Furthermore, the MIL feature allows the system to work on commercial microcontrollers, which reflects the real‐world problems that control algorithms would encounter in actual deployment circumstances. …”
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Developing a high-performance AI model for spontaneous intracerebral hemorrhage mortality prediction using machine learning in ICU settings by Xiao-Han Vivian Yap, Kuan-Chi Tu, Nai-Ching Chen, Che-Chuan Wang, Chia-Jung Chen, Chung-Feng Liu, Tee-Tau Eric Nya, Ching-Lung Kuo

“…This study aims to construct a mortality prediction model for patients with SICH using four various artificial intelligence (AI) machine learning algorithms. Method A retrospective analysis was conducted on electronic medical records of SICH patients aged 20 and above, admitted to Chi Mei Medical Center’s intensive care unit between January 2016 and December 2021. …”
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Association between pace of biological aging and cancer and the modulating role of physical activity: a national cross-sectional study by Jingying Nong, Yu Wang, Yi Zhang

“…Epigenetic clocks, derived from sets of DNA methylation CpGs and mathematical algorithms, have demonstrated a remarkable ability to indicate biological aging and age-related health risks. …”
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Technology Requirements for an Alamouti-Coded 100 Gb/s Digital Coherent Receiver Using 3 × 3 Couplers for Passive Optical Networks by Md. Saifuddin Faruk, David J. Ives, Seb J. Savory

“…In addition, we present the adaptive DSP algorithms required to recover data for a single-carrier Alamouti-coded signal. …”
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Retrieving the Leaf Area Index of Dense and Highly Clumped Moso Bamboo Canopies from Sentinel-2 MSI Data by Weiliang Fan, Jun Wu, Guang Zheng, Qian Zhang, Xiaojun Xu, Huaqiang Du, Mengxiang Zheng, Kexin Zhang, Feng Zhang

“…These results indicate the following: (1) Depending on gap analysis-based clumping index (Ω) algorithms, leaf clumping corrections in indirect ground-based LAI estimations are unsuccessful for highly clumped Moso bamboo canopies due to heavy overlapped leaves; (2) LAIs of dense and highly clumped Moso bamboo canopies can be retrieved from satellite remote sensing data through canopy reflectance models with leaf clumping consideration; (3) The misunderstanding of LAI ranges of Moso bamboo canopies by previous studies (2.2–6.5) can be attributed to the application of gap analysis-based Ω for indirect ground-based LAI estimations; and (4) Effective leaf area index (L_e) derived from satellite remote sensing data, and validated using gap analysis-based L_e/Ω, could be erroneously interpreted as LAI.…”
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A Cross-Sectional Analysis of Oil Pulling on YouTube Shorts by Jun Yaung, Sun Ha Park, Shahed Al Khalifah

“…<b>Methods</b>: On 28 January 2025, a systematic search of YouTube Shorts was performed using the term “oil pulling” in incognito mode to reduce algorithmic bias. English language videos with at least 1000 views were included through purposive sampling. …”
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Machine learning methods, applications and economic analysis to predict heart failure hospitalisation risk: a scoping review by Joana Seringa, João Abreu, Teresa Magalhaes

“…All included studies employed supervised ML algorithms, with ensemble-based methods generally yielding the highest predictive performance. …”
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