Background Remission and low-disease activity are recommended targets in systemic lupus erythematosus (SLE), yet many ...
Background Patients with heart failure (HF) frequently suffer from undetected declines in cardiorespiratory fitness (CRF), which significantly increases their risk of poor outcomes. However, current ...
A Hybrid Machine Learning Framework for Early Diabetes Prediction in Sierra Leone Using Feature Selection and Soft-Voting Ensemble ...
Stroke is one of the leading causes of death and disability worldwide, making early screening and risk prediction crucial. Traditional methods have limitations in handling nonlinear relationships ...
Data from GSE151371, GSE47681, and 10 HLMRGs were analyzed. Subsequently, biomarkers were identified based on receiver operating characteristic (ROC) curves, followed by logistic regression modeling ...
ABSTRACT: In order to solve the problem of chronic heart failure risk prediction in the elderly, a logistic regression modeling framework with Bayesian method was proposed, aiming to solve the problem ...
As biomarker studies employ increasingly complex and expensive genomics and other correlative methods, it is increasingly important to rigorously design these studies and analyze the downstream ...
This project explores and evaluates multiple classification algorithms, including K-Nearest Neighbors (KNN), Logistic Regression, Support Vector Machines (SVM), and ensemble methods (Boosting and ...
In today's digital landscape, organizations face an unprecedented challenge: managing and protecting ever-growing volumes of data spread across multiple environments. As someone deeply involved in ...
Abstract: In bioinformatics, the rapid development of sequencing technology has enabled us to collect an increasing amount of omics data. Classification based on omics data is one of the central ...
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