AIMC Topic: Random Forest

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Dementia risk predictions from German claims data using methods of machine learning.

Alzheimer's & dementia : the journal of the Alzheimer's Association
INTRODUCTION: We examined whether German claims data are suitable for dementia risk prediction, how machine learning (ML) compares to classical regression, and what the important predictors for dementia risk are.

Predicting Procedure Step Performance From Operator and Text Features: A Critical First Step Toward Machine Learning-Driven Procedure Design.

Human factors
OBJECTIVE: The goal of this study is to assess machine learning for predicting procedure performance from operator and procedure characteristics.

Study of the sensory profile of Coffea canephora through malting/fermentation using HS-SPME-GC-MS and synthetic sampling combined with random forest.

Food chemistry
The sensory attributes of Coffea canephora beverages depend on the chemical composition of the bean, especially to the content of volatile organic compounds (VOCs). However, the relative abundance of these compounds may vary with the stage of bean ma...

Application of machine learning in microwave remediation of total petroleum hydrocarbon contaminated soil: Prediction and key factor identification.

Journal of environmental management
Microwave thermal remediation (TPH) is a promising remediation method for petroleum hydrocarbon contaminated soils due to its high energy efficiency and rapid heating capacity. However, the complexity of influencing factors and their nonlinear intera...

Improving prediction of fragility fractures in postmenopausal women using random forest.

Computers in biology and medicine
Osteoporosis is a chronic disease characterized by a progressive decline in bone density and quality, leading to increased bone fragility and a higher susceptibility to fractures, even in response to minimal trauma. Osteoporotic fractures represent a...

Improving brain tumor diagnosis: A self-calibrated 1D residual network with random forest integration.

Brain research
Medical specialists need to perform precise MRI analysis for accurate diagnosis of brain tumors. Current research has developed multiple artificial intelligence (AI) techniques for the process automation of brain tumor identification. However, existi...

Identifying environmental drivers of Aedes aegypti and Aedes albopictus abundance in the Dallas-Fort Worth metroplex using Random Forest modeling.

Journal of medical entomology
Aedes aegypti and Aedes albopictus are 2 medically important vectors that have established populations globally. In the United States, Ae. aegypti populations declined post-Ae. albopictus introduction, though both species now can be readily found thr...

iAVP-RFVOT: Identify Antiviral Peptides by Random Forest Voting Machine Learning with Unified Manifold Learning Embedded Features.

Biochemistry
Viruses are transmitted through multiple routes and can cause a wide range of diseases. Antiviral peptides (AVPs) have emerged as a cost-effective and low-side-effect strategy for combating viral infections. However, identifying antiviral peptides ex...

Predicting response to anti-VEGF therapy in neovascular age-related macular degeneration using random forest and SHAP algorithms.

Photodiagnosis and photodynamic therapy
PURPOSE: This study aimed to establish and validate a prediction model based on machine learning methods and SHAP algorithm to predict response to anti-vascular endothelial growth factor (VEGF) therapy in neovascular age-related macular degeneration ...

Exploring the diagnostic potential of EEG theta power and interhemispheric correlation of temporal lobe activities in Alzheimer's Disease through random forest analysis.

Computers in biology and medicine
BACKGROUND: Considering the prevalence of Alzheimer's Disease (AD) among the aging population and the limited means of treatment, early detection emerges as a crucial focus area whereas electroencephalography (EEG) provides a promising diagnostic too...