AIMC Topic: Decision Trees

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Evaluating distributed-learning on real-world obstetrics data: comparing distributed, centralized and local models.

Scientific reports
This study focused on comparing distributed learning models with centralized and local models, assessing their efficacy in predicting specific delivery and patient-related outcomes in obstetrics using real-world data. The predictions focus on key mom...

Explainable machine learning approach for cancer prediction through binarilization of RNA sequencing data.

PloS one
In recent years, researchers have proven the effectiveness and speediness of machine learning-based cancer diagnosis models. However, it is difficult to explain the results generated by machine learning models, especially ones that utilized complex h...

Accurate fall risk classification in elderly using one gait cycle data and machine learning.

Clinical biomechanics (Bristol, Avon)
BACKGROUND: Falls among the elderly are a major societal problem. While observations of medium-distance walking using inertial sensors identified potential fall predictors, classifying individuals at risk based on single gait cycles remains elusive. ...

New perspectives in the differential diagnosis of jaw lesions: Machine learning and inflammatory biomarkers.

Journal of stomatology, oral and maxillofacial surgery
This study aimed to assess the diagnostic performance of a machine learning approach that utilized radiomic features extracted from Cone Beam Computer Tomography (CBCT) images and inflammatory biomarkers for distinguishing between Dentigerous Cysts (...

Machine learning models to predict submucosal invasion in early gastric cancer based on endoscopy features and standardized color metrics.

Scientific reports
Conventional endoscopy is widely used in the diagnosis of early gastric cancers (EGCs), but the graphical features were loosely defined and dependent on endoscopists' experience. We aim to establish a more accurate predictive model for infiltration d...

The Machine Learning Model for Predicting Inadequate Bowel Preparation Before Colonoscopy: A Multicenter Prospective Study.

Clinical and translational gastroenterology
INTRODUCTION: Colonoscopy is a critical diagnostic tool for colorectal diseases; however, its effectiveness depends on adequate bowel preparation (BP). This study aimed to develop a machine learning predictive model based on Chinese adults for inadeq...

Leveraging machine learning: Covariate-adjusted Bayesian adaptive randomization and subgroup discovery in multi-arm survival trials.

Contemporary clinical trials
Clinical trials evaluate the safety and efficacy of treatments for specific diseases. Ensuring these studies are well-powered is crucial for identifying superior treatments. With the rise of personalized medicine, treatment efficacy may vary based on...

Predicting community acquired bloodstream infection in infants using full blood count parameters and C-reactive protein; a machine learning study.

European journal of pediatrics
Early recognition of bloodstream infection (BSI) in infants can be difficult, as symptoms may be non-specific, and culture can take up to 48 h. As a result, many infants receive unneeded antibiotic treatment while awaiting the culture results. In thi...

Explainable and visualizable machine learning models to predict biochemical recurrence of prostate cancer.

Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico
PURPOSE: Machine learning (ML) models presented an excellent performance in the prognosis prediction. However, the black box characteristic of ML models limited the clinical applications. Here, we aimed to establish explainable and visualizable ML mo...

Optimization of computational intelligence approach for the prediction of glutinous rice dehydration.

Journal of the science of food and agriculture
BACKGROUND: Five computational intelligence approaches, namely Gaussian process regression (GPR), artificial neural network (ANN), decision tree (DT), ensemble of trees (EoT) and support vector machine (SVM), were used to describe the evolution of mo...