Machine learning-based detection of medical service anomalies: Kazakhstan’s health insurance data by Maksut Kulzhanov, Alexander Wagner, Abylkair Skakov, Iliyas Mukhamejan, Saya Zhorabek, Ainur B. Qumar

“…With the exponential growth of medical data and limited analytical resources, healthcare systems are increasingly adopting Artificial Intelligence (AI) and Machine Learning (ML) technologies to enhance their decision-making processes. This research aims to apply advanced ML algorithms to analyze data from the Republic of Kazakhstan’s Obligatory Health Insurance Fund (OHIF) and automatically detect anomalies in the structure of delivered medical services. …”
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A novel multi-agent dynamic portfolio optimization learning system based on hierarchical deep reinforcement learning by Ruoyu Sun, Yue Xi, Angelos Stefanidis, Zhengyong Jiang, Jionglong Su

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Prediction of clinical pregnancy after frozen embryo transfer based on ultrasound radiomics: an analysis based on the optimal periendometrial zone by Fangfang Xu, Ying Zhang, Qianqing Ma, Lili Hu, Yu Li, Chuanfen Gao, Peipei Guo, Xianyue Yang, Yi Zhou, Jie Zhang, Heng Wang, Chaoxue Zhang

“…We evaluated the performance of the three models using the area under the curve (AUC). …”
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A segmentation method for LiDAR point clouds of aerial slender targets by Birong Huang, Zilong Wang, Jianhua Chen, Bingyang Zhou, Hao Ma

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SbD4Skin by EosCloud: Integrating multi-view molecular representation for predicting skin sensitization, irritation, and acute dermal toxicity by Nikoletta-Maria Koutroumpa, Dimitra-Danai Varsou, Panagiotis D. Kolokathis, Maria Antoniou, Konstantinos D. Papavasileiou, Eleni Papadopoulou, Anastasios G. Papadiamantis, Andreas Tsoumanis, Georgia Melagraki, Milica Velimirovic, Antreas Afantitis

“…Different molecular representations for skin toxicity-related endpoints were first evaluated using three machine learning algorithms (Random Forest, Support Vector Machine, and k-Nearest Neighbors), then combined into a unified input space for training a fully connected neural network (FCNN). …”
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Machine learning-based prediction of optimal antenatal care utilization among reproductive women in Nigeria by Jamilu Sani, Adeyemi Oluwagbemiga, Mohamed Mustaf Ahmed

“…After data preprocessing and feature selection, six supervised ML algorithms—Logistic Regression, Support Vector Machine, K-Nearest Neighbors, Decision Tree, Random Forest, and XGBoost—were applied using Python 3.9. …”
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Determination of Flood Subsidy (2023/2024) Based on SAR Images for Agricultural Land in Lower Saxony, Germany by C.-H. Yang, C. Stemmler, C. Röttger, C. Büker

“…A GIS-based decision tree further refined the results by excluding permanent water bodies. …”
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Jointly Optimizing Resource Allocation, User Scheduling, and Grouping in SBMA Networks: A PSO Approach by Jianjian Wu, Chanzi Liu, Xindi Wang, Chi-Tsun Cheng, Qingfeng Zhou

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Digital Twin-Enabled Blockage-Aware Dynamic mmWave Multi-Hop V2X Communication by Supat Roongpraiwan, Zongdian Li, Tao Yu, Kei Sakaguchi

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Development of the global competitiveness of Russian regions: the concept and configuration of the "smart" complementarity by M. V. Kurnikova

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Development and validation of a machine learning-based nomogram for survival prediction of patients with hilar cholangiocarcinoma after curative-intent resection by Yubo Ma, Qi Li, Zhenqi Tang, Kangpeng Li, Chen Chen, Jianjun Lei, Dong Zhang, Zhimin Geng

“…The patients were randomly assigned to a training set and a testing set in a 7:3 ratio. Risk factors selection was performed by five machine learning (ML) algorithms, including Least Absolute Shrinkage and Selection Operator (LASSO) Regression, Forward Stepwise Cox regression, Boruta feature selection, Random Forest and eXtreme Gradient Boosting (XGBoost). …”
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On the Total Version of Triple Roman Domination in Graphs by Juan Carlos Valenzuela-Tripodoro, Maria Antonia Mateos-Camacho, Martin Cera, Maria Pilar Alvarez-Ruiz

“…We investigate the algorithmic complexity of the associated decision problem, establish sharp bounds regarding graph structural parameters, and obtain the exact values for several graph families.…”
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AI in dermatology: a comprehensive review into skin cancer detection by Kavita Behara, Ernest Bhero, John Terhile Agee

“…We evaluated publications from three prominent journal databases: Scopus, IEEE, and MDPI. …”
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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. …”
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Deciphering Socio-Spatial Integration Governance of Community Regeneration: A Multi-Dimensional Evaluation Using GBDT and MGWR to Address Non-Linear Dynamics and Spatial Heterogene... by Hong Ni, Jiana Liu, Haoran Li, Jinliu Chen, Pengcheng Li, Nan Li

“…Through rigorous spatial econometric modeling, this research uncovers three transformative insights: (1) Urban environment exerts a dominant influence on life satisfaction, accounting for 52.61% of the variance. …”
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Intelligent Prediction Platform for Sepsis Risk Based on Real-Time Dynamic Temporal Features: Design Study by Mingwei Zhang, Ming Zhong, Yunzhang Cheng, Tianyi Zhang

“…Three linear parameters (mean, SD, and endpoint value) were calculated to construct the prediction model using multiple ML algorithms. …”
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Prediction of outcomes following intravenous thrombolysis in patients with acute ischemic stroke using serum UCH-L1, S100β, and NSE: a multicenter prospective cohort study employin... by Ming-Ya Luo, Yang Qu, Peng Zhang, Reziya Abuduxukuer, Li-Juan Wang, Li-Chong Yang, Zhi-Guo Li, Xiao-Dong Liu, Ce Han, Dan Li, Wei-Jia Wang, Dian-Ping Lv, Ming Liu, Jian Gao, Jing Xu, Yongfei Jiang, Hai-Nan Chen, Fu-Jin Li, Li-Ming Sun, Qi-Dong Sun, Yingbin Qi, Si-Yin Sun, Yu Zhang, Zhen-Ni Guo, Yi Yang

“…Least Absolute Shrinkage and Selection Operator regression was used for feature selection, and six ML algorithms were tested. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), F 1-score, calibration curve, and decision curve analysis. …”
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Explainable machine learning model for predicting the transarterial chemoembolization response and subtypes of hepatocellular carcinoma patients by Yunjie Zhang, Songjian Tong, Junhui Yang, Jiawei Lin, Yifan Kong, Deyu Lu, Yan Chen, Yingchao Li, Linfeng Xu, Xiuyan Kong, Guoqing Zhu, Hao Zhang, Pixu Liu, Zhijie Yu, Jinglin Xia

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Optimizing 5-aminosalicylate for moderate ulcerative colitis: expert recommendations from the Asia-Pacific, Middle East, and Africa Inflammatory Bowel Disease Coalition by Filiz Akyüz, Yoon Kyo An, Jakob Begun, Satimai Aniwan, Huu Hoang Bui, Webber Chan, Chang Hwan Choi, Nazeer Chopdat, Susan J Connor, Devendra Desai, Emma Flanagan, Taku Kobayashi, Allen Yu-Hung Lai, Rupert W Leong, Alex Hwong-Ruey Leow, Wai Keung Leung, Julajak Limsrivilai, Virly Nanda Muzellina, Kiran Peddi, Zhihua Ran, Shu Chen Wei, Jose Sollano, Michelle Mui Hian Teo, Kaichun Wu, Byong Duk Ye, Choon Jin Ooi

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Development and validation of a machine-learning model for the risk of potentially inappropriate medications in elderly stroke patients by Xiaodan Yang, Qianqian Ye, Mengxiang Zhang, Yuewei Xu, Manqin Yang

“…ObjectiveTo construct a risk prediction model for potentially inappropriate medications (PIM) in elderly stroke patients based on multiple machine-learning algorithms, providing decision support to identify high-risk patients and ensure rational clinical medication use.MethodsA total of 1,252 discharged stroke patients from a tertiary hospital in Anhui Province, China, were included from January 2023 to December 2024. …”
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