The proper use of model evaluation metrics is important for model evaluation and model selection in binary classification tasks. This study investigates how consistent different metrics are at evaluating models across data of different prevalence whi...
Even though deep learning shows impressive results in several applications, its use on problems with High Dimensions and Low Sample Size, such as diagnosing rare diseases, leads to overfitting. One solution often proposed is feature selection. In dee...
AIM: To compare the effectiveness of artificial neural network (ANN) and traditional statistical analysis on identical data sets within the splenectomy-middle carotid artery occlusion (MCAO) mouse model.
The international journal of biostatistics
39069791
A two-group comparison test is generally performed on RNA sequencing data to detect differentially expressed genes (DEGs). However, the accuracy of this method is low due to the small sample size. To address this, we propose a method using fuzzy clus...
BACKGROUND: One of the most promising approaches for early and more precise disease prediction and diagnosis is through the inclusion of proteomics data augmented with clinical data. Clinical proteomics data is often characterized by its high dimensi...
BACKGROUND AND OBJECTIVES: Having a sufficient sample size is crucial when developing a clinical prediction model. We reviewed details of sample size in studies developing prediction models for binary outcomes using machine learning (ML) methods with...
Support vector machine (SVM) is one of the most popular classification algorithms in the machine learning literature. We demonstrate that SVM can be used to balance covariates and estimate average causal effects under the unconfoundedness assumption....
The British journal of mathematical and statistical psychology
39352067
Over several years, the evaluation of polytomous attributes in small-sample settings has posed a challenge to the application of cognitive diagnosis models. To enhance classification precision, the support vector machine (SVM) was introduced for esti...
Accurate sample classification using transcriptomics data is crucial for advancing personalized medicine. Achieving this goal necessitates determining a suitable sample size that ensures adequate classification accuracy without undue resource allocat...
Computer vision techniques can expedite the detection of bacterial growth in wastewater treatment plants and alleviate some of the shortcomings associated with traditional detection methods. In recent years, researchers capitalized on this potential ...