IEEE

Optimizing Gradient Boosting Algorithms for Obesity Risk Prediction: A Comparative Analysis of XGBoost, LightGBM, and CatBoost Models

Abstract: The importance of should be given to the detection of obesity risk. In the context of this study, the comparison of three machine learning models to the detection of obesity risk ...
Frontiers

Precision dosing of voriconazole in immunocompromised children under 2 years: integrated machine learning and population pharmacokinetic modeling

Objective: This study aimed to develop an individualized dosing strategy for voriconazole (VRZ) in children under 2 years of age by integrating machine learning (ML) and population pharmacokinetic ...
Frontiers

Characterizing clinical risk profiles of major complications in type 2 diabetes mellitus using deep learning algorithms

Objective: To develop a self-reportable risk assessment tool for elderly type 2 diabetes mellitus (T2DM) patients, evaluating risks of diabetic nephropathy (DN), retinopathy (DR), peripheral ...
Forbes

An Algorithm For Controlling Epidemics

When an outbreak of an infectious disease begins to spread, policymakers face an unenviable challenge. They must decide when to intervene and how forcefully, knowing that their choices carry both ...
IEEE

Adaptive Load Shedding Based on Voltage Stability in the Tambora System Using the XGBoost Algorithm

Abstract: Modern power systems require adaptive defense mechanisms to maintain stability during disturbances. This paper proposes an Adaptive Load Shedding (ALS) strategy based on voltage and ...