Based on features extracted from Raman spectra, regularization algorithms, SVR, DT, RF,
LightGBM, CatBoost, and XGBoost were used to develop prediction models for lignin …

The prevalence of diabetes has been increasing in recent years, and previous research has
found that machine-learning models are good diabetes prediction tools. The purpose of this …

ML algorithms are used to develop prognostic and diagnostic models and so to support
clinical decision-making. This study uses eight supervised ML algorithms to predict the need …

Autism spectrum disorder (ASD), developmental language disorder (DLD), and global
developmental delay (GDD) are common neurodevelopmental disorders in early childhood; …

Selecting the best planting area for blueberries is an essential issue in agriculture. To better
improve the effectiveness of blueberry cultivation, a machine learning-based classification …

Background Liver transplantation surgery is often accompanied by massive blood loss and
massive transfusion (MT), while MT can cause many serious complications related to high …

Background Depression is a common mental health problem among veterans, with high
mortality. Despite the numerous conducted investigations, the prediction and identification of …

Objective: We aimed to develop and validate a predictive machine learning (ML) model for
cardiac surgery associated with acute kidney injury (CSA-AKI) based on a multicenter …

BACKGROUND. Prediction of outcomes in patients with aneurysmal subarachnoid
hemorrhage (aSAH) is challenging using current clinical predictors. OBJECTIVE. The …

This study demonstrates how a colorimetric biosensor based on microfluidic paper can
swiftly diagnose a disease and predict its prognosis to triage patients effectively. This was …