Modeling the determinants of attrition in a two-stage epilepsy prevalence survey in Nairobi using machine learning by Daniel M. Mwanga, Isaac C. Kipchirchir, George O. Muhua, Charles R. Newton, Damazo T. Kadengye, Abankwah Junior, Albert Akpalu, Arjune Sen, Bruno Mmbando, Charles R. Newton, Cynthia Sottie, Dan Bhwana, Daniel Mtai Mwanga, Damazo T. Kadengye, Daniel Nana Yaw, David McDaid, Dorcas Muli, Emmanuel Darkwa, Frederick Murunga Wekesah, Gershim Asiki, Gergana Manolova, Guillaume Pages, Helen Cross, Henrika Kimambo, Isolide S. Massawe, Josemir W. Sander, Mary Bitta, Mercy Atieno, Neerja Chowdhary, Patrick Adjei, Peter O. Otieno, Ryan Wagner, Richard Walker, Sabina Asiamah, Samuel Iddi, Simone Grassi, Sloan Mahone, Sonia Vallentin, Stella Waruingi, Symon Kariuki, Tarun Dua, Thomas Kwasa, Timothy Denison, Tony Godi, Vivian Mushi, William Matuja

“…The dataset was split into training and testing sets (7:3 ratio), and seven machine learning models, including the ensemble Super Learner, were trained. …”
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Machine learning to improve HIV screening using routine data in Kenya by Jonathan D. Friedman, Jonathan M. Mwangi, Kennedy J. Muthoka, Benedette A. Otieno, Jacob O. Odhiambo, Frederick O. Miruka, Lilly M. Nyagah, Pascal M. Mwele, Edmon O. Obat, Gonza O. Omoro, Margaret M. Ndisha, Davies O. Kimanga

“…We generated a stratified 60‐20‐20 train‐validate‐test split to assess model generalizability. We trained four machine learning algorithms including logistic regression, Random Forest, AdaBoost and XGBoost. …”
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Machine learning‐based model for worsening heart failure risk in Chinese chronic heart failure patients by Ziyi Sun, Zihan Wang, Zhangjun Yun, Xiaoning Sun, Jianguo Lin, Xiaoxiao Zhang, Qingqing Wang, Jinlong Duan, Li Huang, Lin Li, Kuiwu Yao

“…Eighty per cent of the data was used for training and 20% for testing. The best models were identified by integrating nine ML algorithms and interpreted using SHAP, and to develop a final risk calculation tool. …”
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Development and validation of an early diagnosis model for severe mycoplasma pneumonia in children based on interpretable machine learning by Si Xie, Mo Wu, Yu Shang, Wenbin Tuo, Jun Wang, Qinzhen Cai, Chunhui Yuan, Cong Yao, Yun Xiang

“…Clinical data were selected through Lasso regression analysis, followed by the application of eight machine learning algorithms to develop early warning model. The accuracy of the model was assessed using validation and prospective cohort. …”
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Stacking ensemble learning models diagnose pulmonary infections using host transcriptome data from metatranscriptomics by Tian Zhang, Ying Deng, Wentao Wang, Zhe Zhao, Yiling Wu, Haoqian Wang, Shutao Xia, Weifang Liao, Weijie Liao

“…Leveraging these characteristic genes, we constructed classification sub-models employing 13 types of machine learning algorithms, and we further integrated these sub-models into stacking-based ensemble models with Lasso regression, resulting in diagnostic models that required only a small set of gene expression inputs. …”
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Network-based predictive models for artificial intelligence: an interpretable application of machine learning techniques in the assessment of depression in stroke patients by Wenwei Zuo, Xuelian Yang

“…In addition, the prediction results of the XGBoost model were interpreted in detail using the SHAP algorithm. …”
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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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AI enhanced diagnostic accuracy and workload reduction in hepatocellular carcinoma screening by Rui-Fang Lu, Chao-Yin She, Dan-Ni He, Mei-Qing Cheng, Ying Wang, Hui Huang, Ya-Dan Lin, Jia-Yi Lv, Si Qin, Ze-Zhi Liu, Zhi-Rong Lu, Wei-Ping Ke, Chao-Qun Li, Han Xiao, Zuo-Feng Xu, Guang-Jian Liu, Hong Yang, Jie Ren, Hai-Bo Wang, Ming-De Lu, Qing-Hua Huang, Li-Da Chen, Wei Wang, Ming Kuang

“…Abstract Hepatocellular carcinoma (HCC) ultrasound screening encounters challenges related to accuracy and the workload of radiologists. …”
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GastroHUN an Endoscopy Dataset of Complete Systematic Screening Protocol for the Stomach by Diego Bravo, Juan Frias, Felipe Vera, Juan Trejos, Carlos Martínez, Martín Gómez, Fabio González, Eduardo Romero

“…The dataset covers 22 anatomical landmarks in the stomach and includes an additional category for unqualified images, making it a valuable resource for AI model development. By providing a robust public dataset and baseline deep learning models for image and sequence classification, GastroHUN serves as a benchmark for future research and aids in the development of more effective algorithms.…”
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Deep Learning-Based Draw-a-Person Intelligence Quotient Screening by Shafaat Hussain, Toqeer Ehsan, Hassan Alhuzali, Ali Al-Laith

“…The primary objective of our research is to streamline the IQ screening process for psychologists by leveraging deep learning algorithms. …”
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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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Development and Validation of a Machine Learning Algorithm for Predicting Diabetes Retinopathy in Patients With Type 2 Diabetes: Algorithm Development Study by Sunyoung Kim, Jaeyu Park, Yejun Son, Hojae Lee, Selin Woo, Myeongcheol Lee, Hayeon Lee, Hyunji Sang, Dong Keon Yon, Sang Youl Rhee

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Bearing Fault Diagnosis Based on Parameter Optimized VMD and ELM with Improved SSA by Yang Sen, Wang Hengdi, Cui Yongcun, Li Chang, Tang Yuanchao

“…Finally, through the screening of coefficients of the variation method, the root mean square value and peak value are constructed as the two-dimensional eigenvalue vector of the first layer, and the sample entropy, kurtosis and root mean square are constructed as the three-dimensional eigenvalue vector of the second layer, which are respectively sent to the limit learning machine ELM for the training and classification of rolling bearing faults.The experiment results show that the proposed algorithm has good fault diagnosis performance,ultimately achieving a classification accuracy of 98.25% and an actual diagnostic accuracy of 93.36%.…”
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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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Early prediction of colorectal adenoma risk: leveraging large-language model for clinical electronic medical record data by Xiaoyu Yang, Jinjian Xu, Hong Ji, Jun Li, Bingqing Yang, Liye Wang

“…Several classical machine learning algorithms were applied in combination with the BGE-M3 large-language model (LLM) for enhanced semantic feature extraction. …”
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AI-Based Prediction of Visual Performance in Rhythmic Gymnasts Using Eye-Tracking Data and Decision Tree Models by Ricardo Bernardez-Vilaboa, F. Javier Povedano-Montero, José Ramon Trillo, Alicia Ruiz-Pomeda, Gema Martínez-Florentín, Juan E. Cedrún-Sánchez

“…Conclusion: The decision tree algorithm achieved the highest performance in predicting short-term fixation stability, but its effectiveness was limited in tasks involving accommodative facility, where other models such as SVM and KNN outperformed it in specific metrics. …”
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A Multi-Mode Recognition Method for Broadband Oscillation Based on Compressed Sensing and EEMD by Jinggeng Gao, Honglei Xu, Yong Yang, Haoming Niu, Jinping Liang, Haiying Dong

“…Finally, we use the EEMD algorithm to decompose the reconstructed signal; the intrinsic mode function (IMF) components containing wideband oscillation information are screened by the energy coefficient, and the wideband oscillation information is identified.…”
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RCFGL: Rapid Condition adaptive Fused Graphical Lasso and application to modeling brain region co-expression networks. by Souvik Seal, Qunhua Li, Elle Butler Basner, Laura M Saba, Katerina Kechris

“…In RCFGL, we incorporate the condition specificity into another popular model for joint network estimation, known as fused multiple graphical lasso (FMGL). …”
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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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Comparative Analysis of Osteoarthritis Therapeutics: A Justification for Harnessing Retrospective Strategies via an Inverted Pyramid Model Approach by Quinn T. Ehlen, Jacob Jahn, Ryan C. Rizk, Thomas M. Best

“…Using a similar approach to other management of chronic diseases, we suggest an “Inverted Pyramid” model algorithm, a structured research and development regimen that prioritizes generating widely effective therapies first, with subsequent refinement of treatments based on the development of patient resistance to these therapies. …”
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