Development and validation of machine learning-based risk prediction models for ICU-acquired weakness: a prospective cohort study by Yimei Zhang, Yu Wang, Jingran Yang, Qinglan Li, Min Zhou, Jiafei Lu, Qiulan Hu, Fang Ma

“…Therefore, this study aims to develop and validate risk prediction models for ICUAW based on multiple machine learning algorithms. Methods Four machine learning algorithms were employed. …”
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Establishing a clinical prediction model for diabetic foot ulcers in type 2 diabetic patients with lower extremity arteriosclerotic occlusion using machine learning by Yubo Wang, Chunyu Jiang, Yi qi Xing, Linxuan Zou, Mingzhi Song, Xueling Qu, Zhuqiang Jia, Lin Zhao, Xin Han, Junwei Zong, Shouyu Wang

“…Subsequently, the dataset was divided into a training set comprising patients before 2020 and a validation set comprising patients in 2020 and onwards. …”
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SLC3A2 as a key anoikis−related gene for prognosis and tumor microenvironment remodeling in melanoma by Xiaojin Liu, Jiaheng Xie, Yingying Xiao

“…A total of 150 anoikis-related genes were identified, and 101 machine learning algorithms and their combinations (including Cox regression, random survival forest, and gradient boosting machine) were systematically evaluated to identify the optimal prognostic model. …”
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Time-Domain Versus Frequency-Embedded EEG Sequences for Sensorimotor BCI Using 1D-CNN by Simanto Saha, Mathias Baumert, Alistair Mcewan

“…The proposed time/frequency feature representation techniques with 1D-CNN outperformed CSP-based algorithms (p-value <0.05). The comparative results suggest the utility of the proposed methods for MI classification, especially for a fully zero-training inter-subject BCI.…”
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Integrating machine learning with mendelian randomization for unveiling causal gene networks in glioblastoma multiforme by Lixin Du, Pan Wang, Xiaoting Qiu, Zhigang Li, Jianlan Ma, Pengfei Chen

“…Methods This study employed a comprehensive analysis approach integrating 113 machine learning algorithms with Mendelian Randomization (MR) analysis to investigate the molecular underpinnings of GBM. …”
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Development of a warning model for drug-induced liver injury in the older patients by Qiaozhi Hu, Qiaozhi Hu, Xiaoqi Li, Xiaoqi Li, Dan Zou, Zhiyao He, Zhiyao He, Ting Xu

“…The dataset was randomly partitioned into a training set (80%, n = 2,880) and an independent testing set (20%, n = 720). …”
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Characterisation of cardiovascular disease (CVD) incidence and machine learning risk prediction in middle-aged and elderly populations: data from the China health and retirement lo... by Qing Huang, Zihao Jiang, Bo Shi, Jiaxu Meng, Li Shu, Fuyong Hu, Jing Mi

“…Five machine learning (ML) algorithms were employed for risk prediction. Data preprocessing included missing value imputation via random forest. …”
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Impacts of Spatial Expansion of Urban and Rural Construction on Typhoon-Directed Economic Losses: Should Land Use Data Be Included in the Assessment? by Siyi Zhou, Zikai Zhao, Jiayue Hu, Fengbao Liu, Kunyuan Zheng

“…Results demonstrate three key findings: (1) By introducing prototype learning, a meta-learning approach, to guide model updates, we achieved precise assessments with small training samples, attaining an MAE of 1.02, representing 58.5–76.1% error reduction compared to conventional machine learning algorithms. …”
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Acute Respiratory Infections Identification With Cough Sounds and Overlapping Patch Modulated Vision Transformers by P. V. V. Kishore, D. Anil Kumar, Pasupuleti Sasikiran, Kaja Krishna Mohan, P. Praveen Kumar, Mogadala Vinod Kumar

“…Variable cough sound vs silent intervals between samples of a class in MFCC spectral images has shown to influence training algorithms to learn meaningful patterns for classification. …”
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Integrin factor (FAM27E3), as a metastatic marker of papillary thyroid carcinoma, through the p53 signaling pathway promoting lymph node metastasis by Dong Zhang, Kai-Fang Xiang, Chen-Chen Hong, Xiao-Ping Bao, Yan Wu, Rui Wu

“…Immune cell infiltration was assessed using the CIBERSORT algorithm. CCK8, colony formation, Transwell assays, Western blotting, and RT-PCR were employed to evaluate cell viability, invasion, migration, and gene/protein expression.ResultsThe clustering method distinguished the samples into two subtypes. …”
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Combining a Risk Factor Score Designed From Electronic Health Records With a Digital Cytology Image Scoring System to Improve Bladder Cancer Detection: Proof-of-Concept Study by Sandie Cabon, Sarra Brihi, Riadh Fezzani, Morgane Pierre-Jean, Marc Cuggia, Guillaume Bouzillé

“…ObjectiveOur team aimed to evaluate the benefit of combining the reuse of massive clinical data to build a risk factor model and a digital cytology image–based model (VisioCyt) for bladder cancer detection. …”
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Integrating structured and unstructured data for livestock price forecasting: a sustainability study from South Korea by Yifan Zhu, Yifan Zhu, Yifan Zhu, Tserenpurev Chuluunsaikhan, Jong-Hyeok Choi, Aziz Nasridinov

“…Data from 2018 to 2020 were used for training, while data from 2021 served as the test set. …”
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Assessing the spatial-temporal performance of machine learning in predicting grapevine water status from Landsat 8 imagery via block-out and date-out cross-validation by Eve Laroche-Pinel, Vincenzo Cianciola, Khushwinder Singh, Gaetano A. Vivaldi, Luca Brillante

“…As a result, grapevine growers require reliable spatial and temporal information on vine water status to adapt practices. This research evaluates the use of Landsat 8 satellite imagery in conjunction with weather data, and a machine learning algorithm (Gradient Boosting Machine) to predict vine water status in large vineyard blocks. …”
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Chemotherapy-related adverse drug reaction and associated factors among adult cancer patient attending Jimma medical center oncology unit, Southwest Ethiopia. by Mukerem Sultan Shrmeka, Mubarik Fetu Semman, Biruk Tafese Moges, Feki Nekir Dereja, Aster Wakjira Garedo

“…The data was collected by one pharmacist and two nurses after giving training. Data was entered into Epidata version 4.6.0 and analyzed by SPSS version 25. …”
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Identification of a Prognosis-Related Risk Signature for Bladder Cancer to Predict Survival and Immune Landscapes by Linhui Wang, Yutao Wang, Jianfeng Wang, Luanfeng Li, Jianbin Bi

“…The Kaplan–Meier survival curves and receiver operating characteristics were used to evaluate the model’s effectiveness. The potential biological functions of the selected genes were analyzed using CIBERSORT and ESTIMATE algorithms. …”
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To accurately predict lymph node metastasis in patients with mass-forming intrahepatic cholangiocarcinoma by using CT radiomics features of tumor habitat subregions by Pengyu Chen, Zhenwei Yang, Peigang Ning, Hao Yuan, Zuochao Qi, Qingshan Li, Bo Meng, Xianzhou Zhang, Haibo Yu

“…The optimal model obtained by fusing the features of the Habitat1 and Habitat5 models had AUC values of 0.923 and 0.913 in the training set and validation set, respectively, demonstrating good predictive ability. …”
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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

“…Before preprocessing the data was normalized, treated with feature engineering and augmentation, and then split into training and test sets. To evaluate performance, the proposed EcoTaskSched model demonstrated superiority by significantly reducing energy consumption and improving job completion rates compared to baseline models. …”
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Bi-modal ultrasound radiomics and habitat analysis enhanced the pre-operative prediction of axillary lymph node burden in patients with early-stage breast cancer by Jing Xu, Pan Qi, Xiaoyan Ou, Qiaoxin Zhong, Zhen-Wen Chen, Yin Wang, Aijiao Yi, Bin Wang

“…All patients were randomly divided into training cohort (165 patients) and test cohort (41 patients). …”
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A Novel Rubber Composite Sleeper-Deformation-Prediction Model Based on Response Surface Method (RSM) and Machine Learning (ML) Techniques by Abdulmumin Ahmed Shuaibu, Zhiping Zeng, Ibrahim Hayatu Hassan, Wang Weidong, Hassan Suleiman Otuoze, Suleiman Abdulhakeem, Bushrah Baba Abdulrahman

“…To ensure accurate ML predictions, the dataset was split into 70% for training and 30% for testing. Model performance was evaluated using the correlation coefficient (R2), mean square error (MSE), root means square error (RMSE), and mean absolute error (MAE). …”
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Contrast-enhanced mammography-based interpretable machine learning model for the prediction of the molecular subtype breast cancers by Mengwei Ma, Weimin Xu, Jun Yang, Bowen Zheng, Chanjuan Wen, Sina Wang, Zeyuan Xu, Genggeng Qin, Weiguo Chen

“…The classification performance was evaluated using the area under the receiver operating characteristic curve. …”
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