AIMC Topic: Boosting Machine Learning Algorithms

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Improving stroke risk prediction by integrating XGBoost, optimized principal component analysis, and explainable artificial intelligence.

BMC medical informatics and decision making Feb 7, 2025
The relevance of the study is due to the growing number of diseases of the cerebrovascular system, in particular stroke, which is one of the leading causes of disability and mortality in the world. To improve stroke risk prediction models in terms of...
Principal Component Analysis Stroke Boosting Machine Learning Algorithms Humans Artificial Intelligence Machine Learning Risk Assessment
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On QSPR analysis of glaucoma drugs using machine learning with XGBoost and regression models.

Computers in biology and medicine Jan 28, 2025
Glaucoma is an irreversible, progressive, degenerative eye disorder arising because of increased intraocular pressure, resulting in eventual vision loss if untreated. The QSPR relates, mathematically, by employing various algorithms, a specified prop...
Humans Glaucoma Machine Learning Algorithms Quantitative Structure-Activity Relationship Boosting Machine Learning Algorithms
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Optimized Adaboost Support Vector Machine-Based Encryption for Securing IoT-Cloud Healthcare Data.

Sensors (Basel, Switzerland) Jan 25, 2025
The Internet of Things (IoT) connects various medical devices that enable remote monitoring, which can improve patient outcomes and help healthcare providers deliver precise diagnoses and better service to patients. However, IoT-based healthcare mana...
Computer Security Humans Cloud Computing Algorithms Boosting Machine Learning Algorithms Delivery of Health Care Support Vector Machine Confidentiality Internet of Things
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Unveiling diabetes onset: Optimized XGBoost with Bayesian optimization for enhanced prediction.

PloS one Jan 24, 2025
Diabetes, a chronic condition affecting millions worldwide, necessitates early intervention to prevent severe complications. While accurately predicting diabetes onset or progression remains challenging due to complex and imbalanced datasets, recent ...
Bayes Theorem Female Humans Boosting Machine Learning Algorithms Male Machine Learning Algorithms Diabetes Mellitus
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Exploratory study of extracellular matrix biomarkers for non-invasive liver fibrosis staging: A machine learning approach with XGBoost and explainable AI.

Clinical biochemistry Dec 13, 2024
BACKGROUND: Novel circulating markers for the non-invasive staging of chronic liver disease (CLD) are in high demand. Although underutilized, extracellular matrix (ECM) components offer significant diagnostic potential. This study evaluates ECM-relat...
Male Middle Aged Humans Liver Cirrhosis Hyaluronic Acid Female Machine Learning Aged Extracellular Matrix Biomarkers Adult Boosting Machine Learning Algorithms
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XGBoost as a reliable machine learning tool for predicting ancestry using autosomal STR profiles - Proof of method.

Forensic science international. Genetics Nov 29, 2024
The aim of this study was to test the validity of a predictive model of ancestry affiliation based on Short Tandem Repeat (STR) profiles. Frequencies of 29 genetic markers from the Promega website for four distinct population groups (African American...
Microsatellite Repeats Genetics, Population DNA Fingerprinting Humans Decision Trees Machine Learning Racial Groups Support Vector Machine Boosting Machine Learning Algorithms
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Nonlinear association between visceral fat metabolism score and heart failure: insights from LightGBM modeling and SHAP-Driven feature interpretation in NHANES.

BMC medical informatics and decision making Jul 1, 2025
OBJECTIVE: Using 2005-2018 NHANES data, this study examined the association between the visceral fat metabolism score (METS-VF) and heart failure (HF) prevalence in U.S. adults, leveraging machine learning (LightGBM/XGBoost) and SHAP for classficatio...
Female Aged United States Adult Intra-Abdominal Fat Humans Heart Failure Male Machine Learning Middle Aged Boosting Machine Learning Algorithms Nutrition Surveys
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Cyclist crash severity modeling: A hybrid approach of XGBoost-SHAP and random parameters logit with heterogeneity in means and variances.

Journal of safety research Jul 1, 2025
INTRODUCTION: Across the globe, policymakers are focusing on boosting sustainable transport options, notably cycling, to foster eco-friendly urban environments. However, the persistent safety challenges cyclists face continues to hinder these efforts...
Young Adult Adult Male Humans Female Models, Statistical Accidents, Traffic Safety Middle Aged Adolescent Logistic Models United Kingdom Bicycling Boosting Machine Learning Algorithms
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Comparative analysis of SWAT and SWAT coupled with XGBoost model using Optuna hyperparameter optimization for nutrient simulation: A case study in the Upper Nan River basin, Thailand.

Journal of environmental management Jul 1, 2025
Agricultural runoff leading to nitrate (NO-N) and orthophosphate (PO-P) contamination poses significant environmental and public health risks. This study integrates the Soil and Water Assessment Tool (SWAT) with eXtreme Gradient Boosting (XGBoost), o...
Boosting Machine Learning Algorithms Agriculture Environmental Monitoring Water Pollutants, Chemical Machine Learning Soil Nutrients Thailand Nitrates Rivers
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Optimizing swine manure composting parameters with integrated CatBoost and XGBoost models: nitrogen loss mitigation and mechanism.

Journal of environmental management Jul 1, 2025
In this study, machine learning was used to optimize the aerobic composting process of swine manure to enhance nitrogen retention and compost maturity in order to meet the demand for high-quality organic fertilizers in sustainable agriculture. In thi...
Composting Swine Agriculture Nitrogen Manure Fertilizers Boosting Machine Learning Algorithms Machine Learning Soil Animals
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