GitHub

A python package for simultaneous Hyperparameters Tuning and Features Selection for Gradient Boosting Models.

Hyperparameters tuning and features selection are two common steps in every machine learning pipeline. Most of the time they are computed separately and independently. This may result in suboptimal ...
IEEE

Stroke prediction using machine learning techniques

Abstract: Stroke is a major reason for disability and mortality across the globe, making initial prediction and intervention critical t o reducing its impact. This project leverages machine learning ...
Frontiers

Integrative single-cell and cell-free plasma RNA transcriptomics identifies biomarkers for early non-invasive AD screening

Data-driven omics approaches have rapidly advanced our understanding of the molecular heterogeneity of Alzheimer’s disease (AD). However, limited by the unavailability of brain tissue, there is an ...
Scientific Research Publishing

Extra Trees Model for Heart Disease Prediction ()

1 Computer Science Department, Babcock University, Ilishan-Remo, Ogun State, Nigeria. 2 Computer Science Department, Adeleke University, Ede, Osun State, Nigeria. 3 Department of Applied Mathematics, ...
Frontiers

Optimizing predictive features using machine learning for early miscarriage risk following single vitrified-warmed blastocyst transfer

A dual-center retrospective analysis of 1,664 SVBT cycles, including 308 early miscarriage cases, was conducted across two reproductive centers. Multiple machine learning models, such as Logistic ...
BMJ

Molecular basis for the disease-modifying effects of belimumab in systemic lupus erythematosus and molecular predictors of early response: blood transcriptome analysis ...

Objectives Belimumab is a putative disease-modifying agent in systemic lupus erythematosus (SLE), yet the molecular underpinnings of its effects and the ability to predict early clinical response ...
snmjournals.org

Exploring the impact of feature selection methods and classification algorithms on the predictive performance of PET radiomic ML models in lung cancer

Introduction: The high-dimensional data from the radiomics technique can lead to overfitting and poor performance in predicting lung cancer mutation status. Thus, finding the best combination of ...