Development of a machine learning model for predicting renal damage in children with closed spinal dysraphism by Yu He, Wan-liang Guo, Ming-chang Zhang

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Prediction of the 180 day functional outcomes in aneurysmal subarachnoid hemorrhage using an optimized XGBoost model by Weichong Zhou, Xingfu Liao, Hui Shi, Mingfeng Wang, Yunchong Xiao, Xilu Yu, Yilong Wu, YanYi Liu, Yin Peng, Hai Su

Subjects: “…The XGBoost algorithm (extreme gradient boosting)…”
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Predictive modeling of asthma drug properties using machine learning and topological indices in a MATLAB based QSPR study by Jalal Hatem Hussein Bayati, Abid Mahboob, Laiba Amin, Muhammad Waheed Rasheed, Abdu Alameri

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Optimizing Curriculum for Students: A Machine Learning Approach to Time Management Analysis by Jianmin Dong

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High-resolution energy consumption forecasting of a university campus power plant based on advanced machine learning techniques by Saad A. Alsamraee, Sanjeev Khanna

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Integrating remote sensing, GIS, and machine learning for zoonotic cutaneous leishmaniasis modelling by Fatemeh Parto Dezfooli, Mohammad Javad Valadan Zoej, Fahimeh Youssefi, Ebrahim Ghaderpour

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Enhancing shear strength predictions of UHPC beams through hybrid machine learning approaches by Sanjog Chhetri Sapkota, Ajad Shrestha, Moinul Haq, Satish Paudel, Waiching Tang, Hesam Kamyab, Daniele Rocchio

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A novel method to predict the haemoglobin concentration after kidney transplantation based on machine learning: prediction model establishment and method optimization by Songping He, Xiangxi Li, Fangyu Peng, Jiazhi Liao, Xia Lu, Hui Guo, Xin Tan, Yanyan Chen

“…Recursive feature elimination and extreme gradient boosting were used to rank and screen the importance of patient features and reduce the dimensionality of the features. …”
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Comparative analysis of machine learning approaches for heatwave event prediction in India by Ritesh Choudary V, Anita Christaline Johnvictor, Prem Sankar N

“…The study evaluates the performance of models including Random Forest, Convolutional Neural Networks, LightGBM, Long Short-Term Memory Networks, Transformer Networks, Support Vector Machines, Graph Neural Networks, Extreme Gradient Boosting and Autoencoders for Anomaly Detection in heatwave. …”
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Development and validation of a predictive model for new HIV infection screening among persons 15 years and above in primary healthcare settings in Kenya: a study protocol by Simon Karanja, Amos Otieno Olwendo, Gideon Kikuvi

“…Inferential analysis will be conducted using algorithms that perform best in disease prediction: Extreme Gradient Boosting (XGBoost) and Multilayer Perceptron. …”
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Robust-tuning machine learning algorithms for precise prediction of permeability impairment due to CaCO3 deposition by Mohammad Javad Khodabakhshi, Masoud Bijani, Masoud Hasani

“…Using machine learning models—Support Vector Regression (SVR), Extra Trees (ET), and Extreme Gradient Boosting (XGB)—the research aims to predict how much permeability is lost due to scaling. …”
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Time series analysis of dengue incidence and its association with meteorological risk factors in Bangladesh. by Kazi Estieque Alam, Md Jisan Ahmed, Ritu Chalise, Md Abdur Rahman, Tasnia Thanim Mathin, Md Ismile Hossain Bhuiyan, Prajwal Bhandari, Delower Hossain

“…Seasonal Autoregressive Integrated Moving Average (SARIMA) and Extreme Gradient Boosting (XGBoost) models were used for forecasting. …”
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Lysosome-derived biomarkers for predicting survival outcome in acute myeloid leukemia by Gongchang Li, Yangyang Miao, Fang Yuan, Weiran Zhang, Yali Wu, Liqiang Zhu

“…By using a variety of machine learning methods including random forest approach, LASSO-COX regression, and extreme gradient boosting (XGBoost), we create a prognostic six-LRGs-related signature (HPS1, BCAN, SLC2A8, DOC2A, CHMP4C, and SLC29A3), which categorized AML patients into two groups with significant survival and tumor microenvironment (TME) differences. …”
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Designing Predictive Analytics Frameworks for Supply Chain Quality Management: A Machine Learning Approach to Defect Rate Optimization by Zainab Nadhim Jawad, Balázs Villányi

“…The framework employs advanced ML algorithms, including extreme gradient boosting (XGBoost), support vector machines (SVMs), and random forests (RFs), to accurately predict defect rates and derive actionable insights for supply chain optimization. …”
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GRID SEARCH AND RANDOM SEARCH HYPERPARAMETER TUNING OPTIMIZATION IN XGBOOST ALGORITHM FOR PARKINSON’S DISEASE CLASSIFICATION by Shafa Fitria Aqilah Khansa, Nurissaidah Ulinnuha, Wika Dianita Utami

“…This study classifies Parkinson's disease using the Extreme Gradient Boosting (XGBoost) algorithm with hyperparameter tuning via Grid Search and Random Search. …”
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DDoS attack detection in intelligent transport systems using adaptive neuro-fuzzy inference system by G. Usha, H. Karthikeyan, Kumar Gautam, Nikhil Pachauri

“…Based on the experimental results, the proposed model achieved 94.3% accuracy, outperforming traditional classifiers such as Support Vector Machine, Random Forest, Extreme Gradient Boosting, and Convolutional Neural Network. …”
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Building Safer Social Spaces: Addressing Body Shaming with LLMs and Explainable AI by Sajedeh Talebi, Neda Abdolvand

“…Fine-tuned Psycho-Robustly Optimized BERT Pretraining Approach (Psycho-RoBERTa), pre-trained on psychological texts, excels (accuracy: 0.98, F1-score: 0.994, AUC: 0.990), surpassing models like Extreme Gradient Boosting (XG-Boost) (accuracy: 0.972) and Convolutional Neural Network (CNN) (accuracy: 0.979) due to its contextual sensitivity. …”
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Applications of Machine Learning Algorithms in Geriatrics by Adrian Stancu, Cosmina-Mihaela Rosca, Emilian Marian Iovanovici

“…The most studied algorithms in research articles are Random Forest, Extreme Gradient Boosting, and support vector machines. They are preferred due to their performance in processing incomplete clinical data. …”
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Data-driven price trends prediction of Ethereum: A hybrid machine learning and signal processing approach by Ebenezer Fiifi Emire Atta Mills, Yuexin Liao, Zihui Deng

“…Hence, compared to models in literature such as Gradient Boosting, Long Short-Term Memory, Random Forest, and Extreme Gradient Boosting, the proposed model adapts to complex data patterns and captures intricate non-linear relationships, making it well-suited for cryptocurrency prediction.…”
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A machine learning framework for predicting healthcare utilization and risk factors by Yead Rahman, Prerna Dua

“…., logistic regression, extreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), random forest, decision tree, artificial neural networks (ANN), and naïve bayes, were evaluated based on predictive performance, computational efficiency, and feature importance. …”
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