AIMC Topic: Random Forest

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Machine learning using random forest to differentiate between blow and fall situations of head trauma.

International journal of legal medicine Feb 22, 2025
Blunt head trauma is a common occurrence in forensic practice. Interpreting the origin of craniocerebral injuries can be a challenging process, particularly when it comes to distinguishing between falls or inflicted blows. The objective of this study...
Humans Machine Learning Female Aged Young Adult Adolescent Logistic Models Random Forest Skull Fractures Head Injuries, Closed Adult Male Middle Aged Accidental Falls
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The machine learning algorithm based on decision tree optimization for pattern recognition in track and field sports.

PloS one Feb 13, 2025
This study aims to solve the problems of insufficient accuracy and low efficiency of the existing methods in sprint pattern recognition to optimize the training and competition strategies of athletes. Firstly, the data collected in this study come fr...
Convolutional Neural Networks Athletes Humans Pattern Recognition, Automated Machine Learning Support Vector Machine Track and Field Computer Simulation Random Forest Biomechanical Phenomena Running
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Machine learning-random forest model was used to construct gene signature associated with cuproptosis to predict the prognosis of gastric cancer.

Scientific reports Feb 4, 2025
Gastric cancer (GC) is one of the most common tumors; one of the reasons for its poor prognosis is that GC cells can resist normal cell death process and therefore develop distant metastasis. Cuproptosis is a novel type of cell death and a limited nu...
Gene Expression Regulation, Neoplastic Nomograms Random Forest Kaplan-Meier Estimate Prognosis Female Stomach Neoplasms Biomarkers, Tumor Male Humans Middle Aged Machine Learning Gene Expression Profiling Transcriptome ROC Curve
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Pre-screening of endomysial microvessel density by fast random forest image processing machine learning algorithm accelerates recognition of a modified vascular network in idiopathic inflammatory myopathies.

Diagnostic pathology Jan 31, 2025
Biomarkers for discrimination among different subgroups of idiopathic inflammatory myopathies (IIM) are difficult to identify and may involve multiple laboratory tests and time-consuming procedures. We assessed the potential for artificial intelligen...
Microvascular Density Myositis Image Interpretation, Computer-Assisted Microvessels Platelet Endothelial Cell Adhesion Molecule-1 Capillaries Middle Aged Image Processing, Computer-Assisted Muscle, Skeletal Random Forest Machine Learning Algorithms Humans Female Adult Male
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Machine learning to detect recent recreational drug use in intensive cardiac care units.

Archives of cardiovascular diseases Jan 30, 2025
BACKGROUND: Although recreational drug use is a strong risk factor for acute cardiovascular events, systematic testing is currently not performed in patients admitted to intensive cardiac care units, with a risk of underdetection. To address this iss...
Predictive Value of Tests Substance-Related Disorders Random Forest Substance Abuse Detection France Aged Male Middle Aged Risk Assessment Machine Learning Humans Illicit Drugs Prospective Studies Coronary Care Units Female Recreational Drug Use Reproducibility of Results Risk Factors
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Machine Learning-Based Real-Time Survival Prediction for Gastric Neuroendocrine Carcinoma.

Annals of surgical oncology Jan 28, 2025
BACKGROUND: This study aimed to develop a dynamic survival prediction model utilizing conditional survival (CS) analysis and machine learning techniques for gastric neuroendocrine carcinomas (GNECs).
Humans Machine Learning Retrospective Studies Female Aged Stomach Neoplasms Survival Rate Male Predictive Learning Models Regression Analysis Middle Aged Random Forest Risk Assessment Carcinoma, Neuroendocrine Nomograms SEER Program United States Proportional Hazards Models
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Predicting postoperative adhesive small bowel obstruction in infants under 3 months with intestinal malrotation: a random forest approach.

Jornal de pediatria Jan 21, 2025
OBJECTIVE: This study aimed to develop a predictive model using a random forest algorithm to determine the likelihood of postoperative adhesive small bowel obstruction (ASBO) in infants under 3 months with intestinal malrotation.
Infant, Newborn Risk Factors Sensitivity and Specificity Male Postoperative Complications Machine Learning Algorithms Retrospective Studies Female Random Forest Intestinal Obstruction Intestine, Small Intestinal Volvulus Infant ROC Curve Predictive Value of Tests Tissue Adhesions Humans
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Deep learning-based skin lesion analysis using hybrid ResUNet++ and modified AlexNet-Random Forest for enhanced segmentation and classification.

PloS one Jan 16, 2025
Skin cancer is considered globally as the most fatal disease. Most likely all the patients who received wrong diagnosis and low-quality treatment die early. Though if it is detected in the early stages the patient has fairly good chance and the afore...
Random Forest Humans Neural Networks, Computer Algorithms Skin Neoplasms Deep Learning Convolutional Neural Networks Image Processing, Computer-Assisted Skin
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Machine learning-based prediction model integrating ultrasound scores and clinical features for the progression to rheumatoid arthritis in patients with undifferentiated arthritis.

Clinical rheumatology Jan 10, 2025
OBJECTIVES: Predicting rheumatoid arthritis (RA) progression in undifferentiated arthritis (UA) patients remains a challenge. Traditional approaches combining clinical assessments and ultrasonography (US) often lack accuracy due to the complex intera...
Severity of Illness Index Ultrasonography Arthritis, Rheumatoid Predictive Value of Tests Random Forest Feasibility Studies Antirheumatic Agents Joints Adult Sensitivity and Specificity Male Middle Aged Disease Progression Humans Machine Learning Prospective Studies
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Facial Expression Recognition for Healthcare Monitoring Systems Using Neural Random Forest.

IEEE journal of biomedical and health informatics Jan 7, 2025
Facial expressions vary with different health conditions, making a facial expression recognition (FER) system valuable within a healthcare framework. Achieving accurate recognition of facial expressions is a considerable challenge due to the difficul...
Humans Random Forest Automated Facial Recognition Facial Expression Neural Networks, Computer Algorithms Databases, Factual Image Processing, Computer-Assisted
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