AIMC Topic: Random Forest

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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 Jun 1, 2025
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...
Humans Female Aged Aged, 80 and over Random Forest Male Alzheimer Disease Middle Aged Temporal Lobe Electroencephalography Theta Rhythm Signal Processing, Computer-Assisted
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Identification and prioritization of disease candidate genes using biomedical named entity recognition and random forest classification.

Computers in biology and medicine Jun 1, 2025
BACKGROUND AND OBJECTIVE: The elucidation of candidate genes is fundamental to comprehending intricate diseases, vital for early diagnosis, personalized treatment, and drug discovery. Traditional Disease Gene Identification methods encounter limitati...
Alzheimer Disease Computational Biology Humans Databases, Genetic Machine Learning Random Forest Genetic Predisposition to Disease Data Mining
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Integrative PPI network and random forest analysis identifies KIF4A, TOP2A, and ASPM protein macromolecules as novel protein biomarkers in renal carcinoma pathogenesis.

International journal of biological macromolecules Jun 1, 2025
The pathogenesis of kidney cancer is not fully understood, so there is an urgent need to identify new biomarkers to improve diagnosis and treatment. The study identified KIF4A, TOP2A and ASPM as novel protein biomarkers for renal cancer and explored ...
Humans Prognosis Poly-ADP-Ribose Binding Proteins Biomarkers, Tumor DNA Topoisomerases, Type II Kidney Neoplasms Gene Expression Regulation, Neoplastic Carcinoma, Renal Cell Nerve Tissue Proteins Kinesins Epithelial-Mesenchymal Transition Random Forest Gene Expression Profiling Cell Line, Tumor Protein Interaction Maps Computational Biology
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Comparison of machine learning models for predicting stroke risk in hypertensive patients: Lasso regression model, random forest model, Boruta algorithm model, and Boruta algorithm combined with Lasso regression model.

Medicine May 30, 2025
The aim of this study was to compare the performance of 4 machine learning models-Lasso regression model, random forest model, Boruta algorithm model, and the Boruta algorithm combined with Lasso regression-in predicting stroke risk among hypertensiv...
Regression Analysis Area Under Curve Random Forest Stroke Middle Aged Female Humans Risk Assessment Risk Factors Machine Learning ROC Curve Aged Algorithms Hypertension Male
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Machine learning approach for differentiating iron deficiency anemia and thalassemia using random forest and gradient boosting algorithms.

Scientific reports May 15, 2025
Formulas based on red blood cell indices have been used to differentiate between iron deficiency anemia (IDA) and thalassemia (Thal). However, they exhibit varying efficiencies. In this study, we aimed to develop a tool for discriminating between IDA...
Humans Machine Learning Algorithms Female Adolescent ROC Curve Diagnosis, Differential Random Forest Young Adult Adult Male Middle Aged Boosting Machine Learning Algorithms Anemia, Iron-Deficiency Erythrocyte Indices Thalassemia
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Random forest-based model for the recurrence prediction of borderline ovarian tumor: clinical development and validation.

Journal of cancer research and clinical oncology May 11, 2025
PURPOSE: This study aims to develop an effective machine learning (ML)-based predictive model for the recurrence of borderline ovarian tumor (BOT), and provide the guidelines of accurate clinical diagnosis and precise treatment for patients.
Neoplasm Recurrence, Local Middle Aged Ovarian Neoplasms Humans Random Forest Machine Learning Female Prognosis Aged Young Adult Adult
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Exploring the Influence of Feature Selection Methods on a Random Forest Model for Gait Time Series Prediction Using Inertial Measurement Units.

Journal of biomechanical engineering Apr 1, 2025
Despite the increasing use of inertial measurement units (IMUs) and machine learning techniques for gait analysis, there remains a gap in which feature selection methods are best tailored for gait time series prediction. This study explores the impac...
Gait Models, Biological Mechanical Phenomena Machine Learning Female Biomechanical Phenomena Male Adolescent Time Factors Child Random Forest Humans
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Mitochondrial mt12361A>G increased risk of metabolic dysfunction-associated steatotic liver disease among non-diabetes.

World journal of gastroenterology Mar 14, 2025
BACKGROUND: Insulin resistance, lipotoxicity, and mitochondrial dysfunction contribute to the pathogenesis of metabolic dysfunction-associated steatotic liver disease (MASLD). Mitochondrial dysfunction impairs oxidative phosphorylation and increases ...
Male Middle Aged Case-Control Studies Cohort Studies Polymorphism, Single Nucleotide Logistic Models Random Forest Non-alcoholic Fatty Liver Disease Humans Adult DNA, Mitochondrial Diabetes Complications Boosting Machine Learning Algorithms
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A comparison of random forest variable selection methods for regression modeling of continuous outcomes.

Briefings in bioinformatics Mar 4, 2025
Random forest (RF) regression is popular machine learning method to develop prediction models for continuous outcomes. Variable selection, also known as feature selection or reduction, involves selecting a subset of predictor variables for modeling. ...
Random Forest Machine Learning Algorithms Computational Biology Regression Analysis Humans
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Identifying Risk Factors for Graft Failure due to Chronic Rejection < 15 Years Post-Transplant in Pediatric Kidney Transplants Using Random Forest Machine-Learning Techniques.

Pediatric transplantation Mar 1, 2025
BACKGROUND: Chronic rejection forms the leading cause of late graft loss in pediatric kidney transplant recipients. Despite improvement in short-term graft outcomes, chronic rejection impedes comparable progress in long-term graft outcomes.
Chronic Disease Graft Rejection Random Forest Adolescent Child, Preschool Child Humans Machine Learning Kidney Transplantation Female Risk Factors Male Infant
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