IEEE

BOTANZ: Improving Agricultural Efficiency Through Random Forest Algorithms

Abstract: By evaluating intricate datasets to maximize plant growth, boost yields, and advance sustainability, smart agriculture—powered by Random Forest machine learning—is transforming botany.
openaccessgovernment

Leveraging machine learning and robust small area estimation methods for stand-level forest inventory

This special report introduces small area estimation (SAE) as a modern approach for producing reliable, stand-level forest inventory information Small area estimation (SAE) is a set of statistical ...
IEEE

Classification Analysis of Stunting Data on Toddlers in Bekasi City Using Logistic Regression and Random Forest

Abstract: Stunting in toddlers is one of the most pressing nutritional issues in Indonesia. This condition, caused by chronic malnutrition, is a key indicator of maternal and infant health, ...
Frontiers

Enhanced risk prediction of femoral head osteonecrosis in the elderly: a comparative study of random forest and logistic regression models

Department of Orthopedics, Shanxi Bethune Hospital, Tongji Shanxi Hospital, Shanxi Academy of Medical Sciences, Third Hospital of Shanxi Medical University, Taiyuan, China Background and aim: ...
Frontiers

A comparative study of risk factors in predictive models for cognitive dysfunction in patients with leukoaraiosis based on machine learning algorithms

Objective: To explore the risk factors of cognitive dysfunction in patients with leukoaraiosis (LA) and to construct a predictive model using machine learning. Methods: A total of 273 patients with LA ...
The American Journal of Managed Care

Study Identifies Key Predictors of Successful CD3+ Cell Apheresis for CAR T Manufacturing in DLBCL

Machine-learning analysis identifies factors predicting T cell collection efficiency in CAR T-cell therapy for DLBCL patients. Higher blood volume, not higher circulating T-cell numbers, correlates ...