Search
Search Results
-
Missing data imputation, prediction, and feature selection in diagnosis of vaginal prolapse
BackgroundData loss often occurs in the collection of clinical data. Directly discarding the incomplete sample may lead to low accuracy of medical...
-
A structured combination of ensemble classifier and filter-based feature selection to improve breast cancer diagnosis
IntroductionAdvances in technology have led to the emergence of computerized diagnostic systems as intelligent medical assistants. Machine learning...
-
Identifying misdiagnosed bipolar disorder using support vector machine: feature selection based on fMRI of follow-up confirmed affective disorders
Nearly a quarter of bipolar disorder (BD) patients were misdiagnosed as major depressive disorder (MDD) patients, which cannot be corrected until...
-
Toward improving the performance of learning by joining feature selection and ensemble classification techniques: an application for cancer diagnosis
IntroductionBreast cancer is known as the most common type of cancer in women, and this has raised the importance of its diagnosis in medical science...
-
Optimizing prognostic factors of five-year survival in gastric cancer patients using feature selection techniques with machine learning algorithms: a comparative study
BackgroundGastric cancer is the most common malignant tumor worldwide and a leading cause of cancer deaths. This neoplasm has a poor prognosis and...
-
Classification of breast tumors by using a novel approach based on deep learning methods and feature selection
PurposeCancer is one of the most insidious diseases that the most important factor in overcoming the cancer is early diagnosis and detection. The...
-
An effective role-oriented binary Walrus Grey Wolf approach for feature selection in early-stage chronic kidney disease detection
In clinical decision-making for chronic disorders like chronic kidney disease, high variability often leads to uncertainty and negative outcomes....
-
Interpretable instance disease prediction based on causal feature selection and effect analysis
BackgroundIn the big wave of artificial intelligence swee** the world, machine learning has made great achievements in healthcare in the past few...
-
Fecal calprotectin and platelet count predict histologic disease activity in pediatric ulcerative colitis: results from a projection-predictive feature selection
Especially for pediatric patients, proxies of mucosal inflammation are needed. The Pediatric Ulcerative Colitis Activity Index (PUCAI) has been...
-
An Explainable MRI-Radiomic Quantum Neural Network to Differentiate Between Large Brain Metastases and High-Grade Glioma Using Quantum Annealing for Feature Selection
Solitary large brain metastases (LBM) and high-grade gliomas (HGG) are sometimes hard to differentiate on MRI. The management differs significantly...
-
Just Add Data: automated predictive modeling for knowledge discovery and feature selection
Fully automated machine learning (AutoML) for predictive modeling is becoming a reality, giving rise to a whole new field. We present the basic ideas...
-
Forecasting daily emergency department arrivals using high-dimensional multivariate data: a feature selection approach
Background and objectiveEmergency Department (ED) overcrowding is a chronic international issue that is associated with adverse treatment outcomes....
-
A novel EEG-based major depressive disorder detection framework with two-stage feature selection
BackgroundMajor depressive disorder (MDD) is a common mental illness, characterized by persistent depression, sadness, despair, etc., troubling...
-
Interpretability of radiomics models is improved when using feature group selection strategies for predicting molecular and clinical targets in clear-cell renal cell carcinoma: insights from the TRACERx Renal study
BackgroundThe aim of this work is to evaluate the performance of radiomics predictions for a range of molecular, genomic and clinical targets in...
-
A convolutional neural network model for survival prediction based on prognosis-related cascaded Wx feature selection
Great advances in deep learning have provided effective solutions for prediction tasks in the biomedical field. However, accurate prognosis...
-
Metastatic melanoma treated by immunotherapy: discovering prognostic markers from radiomics analysis of pretreatment CT with feature selection and classification
PurposeImmunotherapy has dramatically improved the prognosis of patients with metastatic melanoma (MM). Yet, there is a lack of biomarkers to predict...
-
Identification of clinical factors related to prediction of alcohol use disorder from electronic health records using feature selection methods
BackgroundHigh dimensionality in electronic health records (EHR) causes a significant computational problem for any systematic search for predictive,...
-
Measuring the bias of incorrect application of feature selection when using cross-validation in radiomics
BackgroundMany studies in radiomics are using feature selection methods to identify the most predictive features. At the same time, they employ...
-
Application of Machine Learning Techniques to Help in the Feature Selection Related to Hospital Readmissions of Suicidal Behavior
Suicide was the main source of death from external causes in Spain in 2020, with 3,941 cases. The importance of identifying those mental disorders...
-
Improving dengue fever predictions in Taiwan based on feature selection and random forests
BackgroundDengue fever is a well-studied vector-borne disease in tropical and subtropical areas of the world. Several methods for predicting the...
