AIMC Topic: Random Forest

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Development and validation of an interpretable Random Forest model for predicting recurrence after endoscopic submucosal dissection in superficial oesophageal squamous cell carcinoma.

Annals of medicine
BACKGROUND: Currently, endoscopic submucosal dissection (ESD) has become the preferred treatment for superficial oesophageal squamous cell carcinoma (SESCC). However, due to the residual background mucosa, some patients are still at risk of postopera...

Developing predictive models for COVID-19 positive tests based on the XGBoost and random forest algorithms with internet search data.

BMC public health
BACKGROUND: Although strategies for COVID-19 have shifted towards normalized measures globally, establishing predictive models based on Internet search data remains crucial for swiftly controlling and preventing future outbreaks. This study aims to u...

Prioritization of patients at risk of heart attack using a novel full-objective ITARA based on Random Forest and Decision tree.

Scientific reports
Heart attacks remain a major cause of morbidity and mortality, particularly among middle-aged and older adults, often aggravated by unhealthy lifestyles and limited preventive care. Early identification and prioritization of at-risk individuals are e...

Application of generalized linear mixed effects random forest for identifying risk factors of prediabetes in Tehran Lipid and Glucose Study.

Scientific reports
Prediabetes is a major risk factor for the development of diabetes, defined by blood glucose levels that are elevated but not yet high enough to meet the diagnostic criteria for Diabetes Mellitus. This condition is often clinically "silent" yet it ca...

A hybrid ACO-random forest optimization framework for scalable microalgae biomass estimation using multispectral imaging.

Environmental monitoring and assessment
Accurate estimation of algal biomass is essential for monitoring ecosystem productivity, managing aquaculture systems, and optimizing bioresource applications. However, traditional in situ methods are labor-intensive and spatially limited, while remo...

Machine learning-based groundwater potential mapping and factor analysis in tropical lateritic terrains using self-organizing maps and random forest.

Environmental monitoring and assessment
Groundwater potential mapping is essential for sustainable water resource management, particularly in tropical lateritic terrains where communities depend heavily on groundwater for domestic and agricultural needs. This study delineates groundwater p...

Hemodynamic determinants of postoperative neurocognitive impairment using Random Forest analysis and partial dependence plots.

Scientific reports
This study investigated the effect of hemodynamic data during cardiopulmonary bypass (CPB) on neurocognitive impairment in patients undergoing coronary artery bypass graft (CABG) surgery using machine learning algorithms. Twenty-eight CABG patients w...

Enhanced random forest with geologically-informed feature optimization for complex volcanic rock lithology identification: A case study in the Wangfu Fault Depression, Songliao Basin.

PloS one
Identifying lithologies within the volcanic reservoirs of the Huoshiling Formation (Wangfu Fault Depression, Songliao Basin) remains challenging due to extreme heterogeneity, limited core control, and ambiguous responses on conventional logs. We intr...

Artificial intelligence strategies based on random forests for detecting ischemia-reperfusion injury changes in kidney tissue during intravital imaging.

Scientific reports
This study presents a supervised machine learning approach using a Random Forest classifier to detect ischemia-reperfusion injury (IRI) in kidney tissue based on intravital two-photon microscopy data. A rodent model of unilateral renal IRI was used, ...

Hierarchical random forest model, inflammation and oxidative stress as predictors of the atherogenic index of plasma and diabetes progression.

Scientific reports
Type 2 diabetes mellitus (T2DM) is a chronic metabolic disease that increases the risk of cardiovascular complications. The atherogenic index of plasma (AIP) is a risk marker for T2DM and cardiovascular disease on the basis of lipid profiles. T2DM an...