Construction of a prognostic prediction model for colorectal cancer based on 5-year clinical follow-up data by Boao Xiao, Min Yang, Yao Meng, Weimin Wang, Yuan Chen, Chenglong Yu, Longlong Bai, Lishun Xiao, Yansu Chen

“…Decision tree, random forest, support vector machine, and extreme gradient boosting (XGBoost) models were selected for modeling based on the features identified through recursive feature elimination (RFE). …”
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Effects of poultry by-product meal and complete replacement of fish oil with alternative oils on growth performance and gut health of rainbow trout (Oncorhynchus mykiss): a FEEDNET... by Imam Hasan, Simona Rimoldi, Biagina Chiofalo, Marianna Oteri, Micaela Antonini, Rosangela Armone, Violeta Kalemi, Laura Gasco, Genciana Terova

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Distribution Characteristics and Coupling Relationship Between Soil Erosion and Hydrologic and Sediment Connectivity in Changchong River Basin by LI Jianing, ZHANG Hongli, TIAN Changyuan, ZHANG Yi, ZHA Tonggang

“…[Results] (1) The average soil erosion modulus in the Changchong River Basin was 380 t/(hm2·a), and the soil erosion intensity was mainly slight erosion, which gradually intensified from north to south. (2) The high hydrological and sediment connectivity is mainly distributed in cultivated land, and the opposite is true in forest and grassland land. The higher value is mainly located in the low-lying flat area with low slope and easy water accumulation, while the lower value is mainly in the steep mountainous area. (3) Topographic factors and land use types significantly affected soil erosion and hydrological and sediment connectivity (p<0.01). …”
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Evaluating the impact of roof rainwater harvesting on hydrological connectivity and urban flood mitigation by Quang-Oai Lu, Reza Bahramloo, Jesús Rodrigo-Comino, Jun Wang, Ali Talebi, Quynh Thi Phuong Tran, Afshin Ghahramani, Mehdi Sepehri

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Evaluation of the impact of the environment on the genetic improvement of the buffalo species by Rafael Emilio Rincón-Márquez, Néstor Simón Montiel-Urdaneta, José Raúl Pérez-González

“…On 5 May 2004, a buffalo farm was started in Finca, Florida, in Zulia state’s arid tropical forest zone (DTFZ). Furthermore, on 9 May 2012, the herd was transferred to Finca Miraflores, located in a premontane rainforest zone (PRZ) in Mérida state. …”
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Glossina pallidipes Density and Trypanosome Infection Rate in Arba Minch Zuria District of Gamo Zone, Southern Ethiopia by Ephrem Tora, Wasihun Seyoum, Firew Lejebo

“…Relatively higher Glossina pallidipes and biting flies, respectively, were caught in a wood-grass land (15.87 F/T/D and 3.69 F/T/D) and riverine forest (15.13 F/T/D and 3.42 F/T/D) than bush land vegetation types (13.87 F/T/D and 1.76 F/T/D). …”
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<i>Broussonetia papyrifera</i> Extract Can Be Used as a Raw Material Source for a Sterility Agent for <i>Microtus fortis</i> by Shuangye Wang, Tian Lan, Yunlin Zhao, Wanfu Liu, Tian Huang, Meiwen Zhang, Zhiyuan Hu, Zhenggang Xu

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A new risk assessment model of venous thromboembolism by considering fuzzy population by Xin Wang, Yu-Qing Yang, Xin-Yu Hong, Si-Hua Liu, Jian-Chu Li, Ting Chen, Ju-Hong Shi

“…Sensitivity and specificity of our method was compared with five ML models (support vector machine (SVM), random forest (RF), gradient boosting decision tree (GBDT), logistic regression (LR), and XGBoost) and the Padua model. …”
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Ethnobotanical survey of plants locally used in the control of termite pests among rural communities in northern Uganda by Betty C. Okori, Christine Oryema, Robert Opiro, Acur Amos, Gilbert I. Obici, Karlmax Rutaro, Geoffrey M. Malinga, Eric Sande

“…Abstract Background Termites are the most destructive pests in many agricultural and forest plantations in Uganda. Current control of termites mostly relies on chemical pesticides. …”
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MHRA-MS-3D-ResNet-BiLSTM: A Multi-Head-Residual Attention-Based Multi-Stream Deep Learning Model for Soybean Yield Prediction in the U.S. Using Multi-Source Remote Sensing Data by Mahdiyeh Fathi, Reza Shah-Hosseini, Armin Moghimi, Hossein Arefi

“…This performance surpassed some of the state-of-the-art models like 3D-ResNet-BiLSTM and MS-3D-ResNet-BiLSTM, and other traditional ML methods like Random Forest (RF), XGBoost, and LightGBM. These findings highlight the methodology’s capability to handle multiple RS data types and its role in improving yield predictions, which can be helpful for sustainable agriculture.…”
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Microsatellite instability and somatic gene variant profile in solid organ tumors by Ibrahim Halil Erdogdu, Seda Orenay-Boyacioglu, Olcay Boyacioglu, Nesibe Kahraman-Cetin, Habibe Guler, Merve Turan, Ibrahim Meteoglu

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Identification of EGR1 as a Key Diagnostic Biomarker in Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD) Through Machine Learning and Immune Analysis by Wu X, Pan T, Fang Z, Hui T, Yu X, Liu C, Guo Z, Liu C

“…We employed three machine learning methods—LASSO, SVM, and Random Forest (RF)—to identify hub genes associated with MASLD. …”
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Identificación de áreas con alta biomasa aérea y alta riqueza de especies en bosques nativos del nordeste de Uruguay by Carla E. Ocaño-Silveira, José René Valdez-Lazalde, Rodrigo Duno-de Stefano, Jose Luis Hernández-Stefanoni

“…Para la estimación de la biomasa aérea y la riqueza de especies se utilizaron Modelos Lineales Generalizados, donde las variables de respuesta fueron calculadas utilizando datos de campo del Inventario Forestal Nacional. Las variables explicativas en el modelo se obtuvieron con información espectral, de retrodispersión y de textura derivada de Sentinel-2, y ALOS PALSAR; así como de datos ambientales, de topografía y clima. …”
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A robust multimodal brain MRI-based diagnostic model for migraine: validation across different migraine phases and longitudinal follow-up data by Jong Young Namgung, Eunchan Noh, Yurim Jang, Mi Ji Lee, Bo-yong Park

“…We employed a regularization-based feature selection method combined with a random forest classifier to construct a diagnostic model. …”
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Risk factors and machine learning prediction models for intrahepatic cholestasis of pregnancy by Yingchun Ren, Xiaoying Shan, Gengchao Ding, Ling Ai, Weiying Zhu, Ying Ding, Fuzhou Yu, Yun Chen, Beijiao Wu

“…Thirteen machine learning techniques, including Random Forest, Support Vector Machine, and Artificial Neural Network, were employed. …”
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Marine ecological information prediction by using adjacent location spatiotemporal deep learning model with ensemble learning techniques by Yue-Shan Chang, Shu-Ting Huang, Basanta Haobijam, Satheesh Abimannan, Takayuki Kushida

“…In this study, we evaluate the proposed model's performance using metrics such as Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and the coefficient of determination (R2), alongside comparative analyses against SVR (Support Vector Regression), AdaBoost, and RF (Random Forest) models. The results show that STH-MLR-LSTM achieves the best average prediction results across the six locations. …”
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Assessing national exposure to and impact of glacial lake outburst floods considering uncertainty under data sparsity by H. Chen, Q. Liang, J. Zhao, S. B. Maharjan

“…In the innovative framework, multi-temporal imagery is utilised with a random forest model to extract glacial lake water surfaces. …”
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Influence of long-term supplementation of a formulated botanical blend on growth performance and carcass traits in feedlot steers by Thiago Lauro Maia Ribeiro, Becca B. Grimes Francis, Erin R. DeHaan, Cassidy R. Ross, Justin J. Delver, Forest L. Francis, Jeff S. Heldt, Emma H. Wall, Warren C. Rusche, Zachary K. Smith

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Sunlight exposure practice and its associated factors among infants in Ethiopia, systematic review and meta-analysis. by Fanos Yeshanew Ayele, Natnael Kebede, Asnakew Molla Mekonen, Mengistu Mera Mihiretu, Yawkal Tsega, Elsabeth Addisu, Niguss Cherie, Tesfaye Birhane, Chala Daba, Ahmed Hussien Asfaw, Zinet Abegaz, Abel Endawekie, Anissa Mohammed, Dagnachew Melak, Fekade Demeke Bayou, Husniya Yasin, Aregash Abebayehu Zerga, Fikre Bayu Gebeyehu, Birhanu Wagaye

“…Meta-analysis was conducted by using STATA 17 software. Forest plots were used to present the pooled prevalence of good sunlight exposure practices. …”
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Development and Validation of a Routine Electronic Health Record-Based Delirium Prediction Model for Surgical Patients Without Dementia: Retrospective Case-Control Study by Emma Holler, Christina Ludema, Zina Ben Miled, Molly Rosenberg, Corey Kalbaugh, Malaz Boustani, Sanjay Mohanty

“…We trained logistic regression, random forest, extreme gradient boosting (XGB), and neural network models to predict POD using 143 features derived from routine EHR data available at the time of hospital admission. …”
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