AIMC Topic: Sample Size

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Effect of training sample size, image resolution and epochs on filamentous and floc-forming bacteria classification using machine learning.

Journal of environmental management Mar 9, 2025
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 ...
Machine Learning Wastewater Sample Size Sewage Algorithms Bacteria
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Larger sample sizes are needed when developing a clinical prediction model using machine learning in oncology: methodological systematic review.

Journal of clinical epidemiology Jan 13, 2025
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...
Machine Learning Neoplasms Humans Sample Size Medical Oncology
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Area under the ROC Curve has the most consistent evaluation for binary classification.

PloS one Dec 23, 2024
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...
Area Under Curve Sample Size Data Interpretation, Statistical Evaluation Studies as Topic Machine Learning ROC Curve Models, Statistical
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Addressing statistical challenges in the analysis of proteomics data with extremely small sample size: a simulation study.

BMC genomics Nov 14, 2024
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...
Computer Simulation Proteomics Algorithms Biomarkers Sample Size Humans Machine Learning
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Applying support vector machines to a diagnostic classification model for polytomous attributes in small-sample contexts.

The British journal of mathematical and statistical psychology Oct 1, 2024
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...
Models, Statistical Sample Size Computer Simulation Reproducibility of Results Humans Psychometrics Algorithms Support Vector Machine
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Detecting differentially expressed genes from RNA-seq data using fuzzy clustering.

The international journal of biostatistics Jul 29, 2024
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...
Cluster Analysis Sample Size Sequence Analysis, RNA Fuzzy Logic RNA-Seq Humans Computer Simulation Gene Expression Profiling
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Biobjective gradient descent for feature selection on high dimension, low sample size data.

PloS one Jul 18, 2024
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...
Algorithms Deep Learning Humans Sample Size Neural Networks, Computer
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Automatic categorization of self-acknowledged limitations in randomized controlled trial publications.

Journal of biomedical informatics Mar 26, 2024
OBJECTIVE: Acknowledging study limitations in a scientific publication is a crucial element in scientific transparency and progress. However, limitation reporting is often inadequate. Natural language processing (NLP) methods could support automated ...
Natural Language Processing Sample Size Publications Language
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Enhancing the fairness of AI prediction models by Quasi-Pareto improvement among heterogeneous thyroid nodule population.

Nature communications Mar 4, 2024
Artificial Intelligence (AI) models for medical diagnosis often face challenges of generalizability and fairness. We highlighted the algorithmic unfairness in a large thyroid ultrasound dataset with significant diagnostic performance disparities acro...
Humans Health Facilities Area Under Curve Sample Size Artificial Intelligence Thyroid Nodule
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Toward Generalizable Machine Learning Models in Speech, Language, and Hearing Sciences: Estimating Sample Size and Reducing Overfitting.

Journal of speech, language, and hearing research : JSLHR Feb 22, 2024
PURPOSE: Many studies using machine learning (ML) in speech, language, and hearing sciences rely upon cross-validations with single data splitting. This study's first purpose is to provide quantitative evidence that would incentivize researchers to i...
Hearing Speech Machine Learning Sample Size Language Humans
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