AIMC Topic: Boosting Machine Learning Algorithms

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Body Weight Estimation in Holstein × Zebu Crossbred Heifers: Comparative Analysis of XGBoost and LightGBM Algorithms.

Veterinary medicine and science Jul 1, 2025
This study evaluates the effectiveness of XGBoost and LightGBM algorithms for estimating the live weight of Holstein×Zebu crossbred heifers. The study compares the performance of both algorithms using a wide range of biometric measurements and tests ...
Machine Learning Animal Husbandry Body Weight Boosting Machine Learning Algorithms Algorithms Female Animals
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Prediction of Insulin Resistance in Nondiabetic Population Using LightGBM and Cohort Validation of Its Clinical Value: Cross-Sectional and Retrospective Cohort Study.

JMIR medical informatics Jun 13, 2025
BACKGROUND: Insulin resistance (IR), a precursor to type 2 diabetes and a major risk factor for various chronic diseases, is becoming increasingly prevalent in China due to population aging and unhealthy lifestyles. Current methods like the gold-stan...
Humans Machine Learning Boosting Machine Learning Algorithms Male Middle Aged Blood Glucose Aged Cohort Studies Adult China Insulin Resistance Cross-Sectional Studies Retrospective Studies Female
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MCD-LightGBM System for Intelligent Analyzing Heterogeneous Clinical Drug Therapeutic Effects.

IEEE journal of biomedical and health informatics Jun 1, 2025
Causal effect estimation of individual heterogeneity is a core issue in the field of causal inference, and its application in medicine poses an active and challenging problem. In high-risk decision-making domain such as healthcare, inappropriate trea...
Humans Drug Therapy Machine Learning Algorithms Boosting Machine Learning Algorithms
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Predicting thyroid cancer recurrence using supervised CatBoost: A SHAP-based explainable AI approach.

Medicine May 30, 2025
Recurrence prediction in well-differentiated thyroid cancer remains a clinical challenge, necessitating more accurate and interpretable predictive models. This study investigates the use of a supervised CatBoost classifier to predict recurrence in we...
Supervised Machine Learning Boosting Machine Learning Algorithms ROC Curve Neoplasm Recurrence, Local Aged Adult Male Middle Aged Risk Assessment Thyroid Neoplasms Humans Female
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A predictive model for hospital death in cancer patients with acute pulmonary embolism using XGBoost machine learning and SHAP interpretation.

Scientific reports May 25, 2025
The prediction of in-hospital mortality in cancer patients with acute pulmonary embolism (APE) remains a significant clinical challenge. This study aimed to develop and validate a machine learning model using XGBoost to predict in-hospital mortality ...
Boosting Machine Learning Algorithms Acute Disease Pulmonary Embolism Aged Male Middle Aged Prognosis Neoplasms Humans Retrospective Studies Hospital Mortality Female ROC Curve Machine Learning
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BoostDILI: Extreme Gradient Boost-Powered Drug-Induced Liver Injury Prediction and Structural Alerts Generation.

Chemical research in toxicology May 19, 2025
Over the past 60 years, drug-induced liver injury (DILI) has played a key role in the withdrawal of marketed drugs due to safety concerns. Early prediction of DILI is crucial for developing safer pharmaceuticals, yet current and testing methods are...
Humans Machine Learning Pharmaceutical Preparations United States Food and Drug Administration Boosting Machine Learning Algorithms Chemical and Drug Induced Liver Injury
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Predicting hospital outpatient volume using XGBoost: a machine learning approach.

Scientific reports May 16, 2025
Hospital outpatient volume is influenced by a variety of factors, including environmental conditions and healthcare resource availability. Accurate prediction of outpatient demand can significantly enhance operational efficiency and optimize the allo...
Boosting Machine Learning Algorithms Algorithms Forecasting Humans Outpatients Machine Learning
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XGBOrdinal: An XGBoost Extension for Ordinal Data.

Studies in health technology and informatics May 15, 2025
We propose XGBOrdinal, an extension of XGBoost designed for ordinal classification problems commonly found in fields like medicine, where outcomes are often represented as scores, scales, stages, or grades. The proposed approach builds on the theoret...
Algorithms Boosting Machine Learning Algorithms Humans Machine Learning
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Machine learning approach for differentiating iron deficiency anemia and thalassemia using random forest and gradient boosting algorithms.

Scientific reports May 15, 2025
Formulas based on red blood cell indices have been used to differentiate between iron deficiency anemia (IDA) and thalassemia (Thal). However, they exhibit varying efficiencies. In this study, we aimed to develop a tool for discriminating between IDA...
Middle Aged Adolescent ROC Curve Adult Humans Male Algorithms Diagnosis, Differential Machine Learning Erythrocyte Indices Female Random Forest Thalassemia Young Adult Anemia, Iron-Deficiency Boosting Machine Learning Algorithms
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Multimodal sentiment analysis leveraging the strength of deep neural networks enhanced by the XGBoost classifier.

Computer methods in biomechanics and biomedical engineering May 1, 2025
Multimodal sentiment analysis, an increasingly vital task in the realms of natural language processing and machine learning, addresses the nuanced understanding of emotions and sentiments expressed across diverse data sources. This study presents the...
Boosting Machine Learning Algorithms Deep Learning Emotions Natural Language Processing Humans Neural Networks, Computer
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