Machine learning algorithms for diabetic kidney disease risk predictive model of Chinese patients with type 2 diabetes mellitus by Lu-Xi Zou, Xue Wang, Zhi-Li Hou, Ling Sun, Jiang-Tao Lu

“…Among the seven forecasting models constructed by MLAs, the accuracy of the Light Gradient Boosting Machine (LightGBM) model was the highest, indicated that the LightGBM algorithms might perform the best for predicting 3-year risk of DKD onset.Conclusions Our study could provide powerful tools for early DKD risk prediction, which might help optimize intervention strategies and improve the renal prognosis in T2DM patients.…”
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Analysis of aPTT predictors after unfractionated heparin administration in intensive care units using machine learning models. by Tadashi Kamio, Masaru Ikegami, Megumi Mizuno, Seiichiro Ishii, Hayato Tajima, Yoshihito Machida, Kiyomitsu Fukaguchi

“…This study aimed to develop a machine learning (ML) model to predict activated partial thromboplastin time (aPTT) in ICU patients receiving unfractionated heparin for anticoagulation and to identify key predictive factors.…”
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A machine learning model based on CT radiomics for preoperatively differentiating intrahepatic mass-type cholangiocarcinoma and inflammatory pseudotumours by Xiao-chun Wang, Jing-hong Liang, Xiao-yao Huang, Wen-jian Tang, Yan-mei He, Jun-yuan Zhong, Ling Zhang, Lun Lu

“…A radiomic feature set, a clinical feature set, and a radiomic + clinical feature set were developed, and each was used to construct 14 machine learning models. The optimal hyperparameters were identified using fivefold cross-validation and a grid search. …”
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Analysis of time series characteristics using machine learning model and correlation matrix in the tasks of forecasting the state of forest ecosystems by Gusev Pavel, Tavolzhanskij Alexander, Zolnikov Vladimir, Deniskina Antonina

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Predicting the mechanical performance of industrial waste incorporated sustainable concrete using hybrid machine learning modeling and parametric analyses by Md. Alhaz Uddin, Md. Habibur Rahman Sobuz, Md. Kawsarul Islam Kabbo, Md. Kanan Chowdhury Tilak, Ratan Lal, Md. Selim Reza, Fahad Alsharari, Mohamed AbdelMongy, Masuk Abdullah

“…SHAP and PDP analyses identified coarse aggregate, superplasticizers, water and cement content have high influence on model’s output. Additionally, 150–200 kg/m3 of GGBFS as key factors for optimizing compressive strength. …”
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Machine Learning Model Based on Prognostic Nutritional Index for Predicting Long‐Term Outcomes in Patients With HCC Undergoing Ablation by Nan Zhang, Ke Lin, Bin Qiao, Liwei Yan, Dongdong Jin, Daopeng Yang, Yue Yang, Xiaohua Xie, Xiaoyan Xie, Bowen Zhuang

“…ABSTRACT Aims To develop multiple machine learning (ML) models based on the prognostic nutritional index (PNI) and determine the optimal model for predicting long‐term survival outcomes in hepatocellular carcinoma (HCC) patients after local ablation. …”
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Ensemble Machine Learning Model Prediction and Metaheuristic Optimisation of Oil Spills Using Organic Absorbents: Supporting Sustainable Maritime by Le Quang Dung, Pham Duc, Bui Thi Anh Em, Nguyen Lan Huong, Nguyen Phuoc Quy Phong, Dang Thanh Nam

“…Using Random Forest (RF) and XGBoost models, high R² values (RF: 0.9517–0.9559; XGBoost: 0.9760), minimal errors, and strong generalisation were obtained by predictive modelling. …”
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Big data-driven corporate financial forecasting and decision support: a study of CNN-LSTM machine learning models by Aixiang Yang

“…A practical enterprise case analysis further confirms the model’s effectiveness in improving financial forecasting accuracy, optimizing decision-making, and mitigating financial risks. …”
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Impedance value prediction of carbon nanotube/polystyrene nanocomposites using tree-based machine learning models and the Taguchi technique by Shohreh Jalali, Majid Baniadam, Morteza Maghrebi

“…Machine learning model including Decision Tree, Random Forest, Extreme Gradient Boosting (XGBoost), Categorical Boost (CatBoost), and Light Gradient-Boosting Machine (LightGBM) were employed to enhance predictive capabilities. …”
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A Novel Strategy for Automatic Selection of Cross‐Basin Data to Improve Local Machine Learning‐Based Runoff Models by Congyi Nai, Xingcai Liu, Qiuhong Tang, Liu Liu, Siao Sun, Paul P. J. Gaffney

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Machine learning based predictive model of the risk of Tourette syndrome with SHAP value interpretation: a retrospective observational study by Aimin Li, Yueying Liu, Yufan Luo, Xue Xiao, Wei Xiao, Ruijin Xie, Xianhui Deng, Zhe Chen, Qian Zhou, Yue Gong, Zhen Chen, Hua Xu

“…Feature selection was conducted using Boruta and multivariable logistic regression analyses, and model construction was undertaken employing 9 distinct machine learning algorithms. 10 distinct features were selected for machine learning algorithm development, and our results indicated that the Gradient Boosting Machine algorithm is the optimal model. …”
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Harnessing machine learning for streamflow prediction: A comparative study of advanced models in the Upper Klang River Basin, Malaysia by Napisah Nasir, Dani Irwan, Ali Najah Ahmed, Saerahany Legori Ibrahim, Izihan Ibrahim, Mohsen Sherif, Ahmed El-Shafie

“…These insights can help hydrological authorities and decision-makers refine predictive models and optimize flood mitigation strategies, ultimately contributing to better environmental and community resilience in the region.…”
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Developing a predictive model for septic shock risk in acute pancreatitis patients using interpretable machine learning algorithms by Binglin Song, Ping Liu, Kangrui Fu, Chun Liu

“…To enhance and optimize model interpretability, Shapley Additive Explanations (SHAP) were employed. …”
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Interpretable machine learning models for predicting Ebus battery consumption rates in cold climates with and without diesel auxiliary heating by Kareem Othman, Diego Da Silva, Amer Shalaby, Baher Abdulhai

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Morphological and structural complexity analysis of low-resource English-Turkish language pair using neural machine translation models by Mehmet Acı, Nisa Vuran Sarı, Çiğdem İnan Acı

“…Similar performance trends were observed in the reverse direction, indicating the model’s generalizability. These findings highlight the potential of carefully optimized Transformer-based NMT systems in handling the complexities of morphologically rich, low-resource languages like Turkish in both translation directions.…”
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Performance and Adaptability Testing of Machine Learning Models for Power Transmission Network Fault Diagnosis With Renewable Energy Sources Integration by Rachna Vaish, Umakant Dhar Dwivedi

“…The proposed performance and adaptability testing of potential ML models has been conducted by optimally integrating different sizes of RES into ‘IEEE 9-Bus System’. …”
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Review of Detecting Text generated by ChatGPT Using Machine and Deep-Learning Models: A Tools and Methods Analysis by Shaymaa Dhyaa Aldeen, Thekra Abbas, Ayad Rodhan Abbas

“…It examines more than 60 academic papers, especially research articles published after the model’s release in 2022, and analyzes state-of-the-art machine learning, deep learning, and hybrid approaches for detecting AI-generated text. …”
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Machine Learning Models to Predict Individual Cognitive Load in Collaborative Learning: Combining fNIRS and Eye-Tracking Data by Wenli Chen, Zirou Lin, Lishan Zheng, Mei-Yee Mavis Ho, Farhan Ali, Wei Peng Teo

“…Nine features, derived from both fNIRS and eye-tracking data, were used as input for the models. Results demonstrated that machine learning models could accurately predict individual cognitive load, with the Random Forest model achieving the highest performance (F1 score = 0.84). …”
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Development of a risk prediction model for sepsis-related delirium based on multiple machine learning approaches and an online calculator. by Lang Gao, Guang Dong Wang, Xing Yi Yang, Shi Jun Tong, Xu Jie Wang, Yun Ruo Chen, Jin Ying Bai, Ya Xin Zhang

“…This study aimed to develop and validate an interpretable machine learning model for early prediction of SAD in critically ill patients. …”
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Prediction of Reactivation After Antivascular Endothelial Growth Factor Monotherapy for Retinopathy of Prematurity: Multimodal Machine Learning Model Study by Rong Wu, Yu Zhang, Peijie Huang, Yiying Xie, Jianxun Wang, Shuangyong Wang, Qiuxia Lin, Yichen Bai, Songfu Feng, Nian Cai, Xiaohe Lu

“…The AUCs for the conventional machine learning model were 0.806 and 0.805 in the internal validation and test groups, respectively. …”
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