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 Artificial Intelligence Machine Learning Risk Assessment Humans
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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 Quantitative Structure-Activity Relationship Machine Learning Boosting Machine Learning Algorithms 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...
Internet of Things Computer Security Cloud Computing Boosting Machine Learning Algorithms Delivery of Health Care Support Vector Machine Confidentiality Humans Algorithms
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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 ...
Boosting Machine Learning Algorithms Machine Learning Humans Male Algorithms Diabetes Mellitus Female Bayes Theorem
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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...
Female Aged Adult Machine Learning Humans Hyaluronic Acid Biomarkers Male Extracellular Matrix Middle Aged Boosting Machine Learning Algorithms Liver Cirrhosis
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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...
Boosting Machine Learning Algorithms Microsatellite Repeats Genetics, Population DNA Fingerprinting Humans Support Vector Machine Machine Learning Decision Trees Racial Groups
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Identification of Fusarium sambucinum species complex by surface-enhanced Raman spectroscopy and XGBoost algorithm.

Food chemistry Jul 15, 2025
Rapid and reliable identification of Fusarium fungi is crucial, due to their role in food spoilage and potential toxicity. Traditional identification methods are often time-consuming and resource-intensive. This study explores the use of surface-enha...
Hordeum Boosting Machine Learning Algorithms Spectrum Analysis, Raman Fusarium
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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...
Boosting Machine Learning Algorithms Machine Learning Composting Soil Manure Animals Fertilizers Swine Agriculture Nitrogen
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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 Retrospective Studies Cross-Sectional Studies Male Middle Aged Female Aged Adult Cohort Studies Insulin Resistance Boosting Machine Learning Algorithms China Blood Glucose
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