ArticlePeerJ. Computer science2024
Trade-off between training and testing ratio in machine learning for medical image processing.
Article in PeerJ. Computer science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 2 of them syntheses that pooled it.
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Who cites it
37 citing papers in PubMed, 2 syntheses or guidelines pooled it.
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- Integrative bioinformatics and machine learning reveal hub genes and immune signatures bridging type 2 diabetes mellitus, fracture susceptibility, and osteoblast differentiation dysfunction.Mammalian genome : official journal of the International Mammalian Genome Society · 2026Article
- Comparative evaluation and selection of optimal QSAR-based machine learning model for liver toxicity prediction.Molecular diversity · 2026Article
- Use of machine learning and voice for multiclass classification of Parkinson's disease, chronic obstructive pulmonary disease, and healthy controls.Scientific reports · 2026Article
- Modeling Complex Effects and Individual Variability in Multi-Paradigm fMRI with Nonlinear Mixed Models.bioRxiv : the preprint server for biology · 2026Article
- Explainable attention-based neural network for load forecasting in super smart grids using socioeconomic and power consumption data.Scientific reports · 2026Article
- MRI-based radiomics explainable model for predicting recurrence of limb chronic osteomyelitis in limb bones treated by Masquelet technique.Bone & joint research · 2026Article
- PUMAA: Establishing a protocol for utilizing machine learning in forensic anthropological analyses.Journal of forensic sciences · 2026Article
- Developing and validating ultrasound-based machine-learning models incorporating radiomics features to predict malignancy in adnexal masses.Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology · 2026Observational
- Fractional-order epidemic modeling with a deep neural network framework.Scientific reports · 2026Article
- Multimodal multi-instance learning for cardiopulmonary exercise testing performance prediction.NPJ digital medicine · 2026Article
- Artificial intelligence to investigate metabolomics data for precision medicine.Metabolomics : Official journal of the Metabolomic Society · 2026Review
- Early Prediction of Adverse Stroke Outcomes Using Nonclinical Factors and Missing Data: A Machine Learning Study.Cerebrovascular diseases (Basel, Switzerland) · 2026Article
- MARVpred: machine learning prediction of inhibitors targeting Marburg virus Gene 4 Small ORF protein.BMC infectious diseases · 2026Article
- Generation of deep learning based virtual contrast-enhanced CT images from noncontrast CT images for target volume delineation in cervical cancer radiotherapy.Frontiers in oncology · 2026Article
- Machine learning prediction of ACL loading during the wide lunge: a multifactorial coupling analysis based on kinematic and electromyographic signals.Frontiers in bioengineering and biotechnology · 2026Article
- A fully automated explainable predictive model for diagnosing pre-capillary and post-capillary pulmonary hypertension on routine unenhanced CT: results from the ASPIRE registry.European heart journal. Digital health · 2026Article
Corrections and comments
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Authors and funding
3 authors.
Funding
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Abstract
Artificial intelligence (AI) and machine learning (ML) aim to mimic human intelligence and enhance decision making processes across various fields. A key performance determinant in a ML model is the ratio between the training and testing dataset. This research investigates the impact of varying train-test split ratios on machine learning model performance and generalization capabilities using the BraTS 2013 dataset. Logistic regression, random forest, k nearest neighbors, and support vector machines were trained with split ratios ranging from 60:40 to 95:05. Findings reveal significant variations in accuracies across these ratios, emphasizing the critical need to strike a balance to avoid overfitting or underfitting. The study underscores the importance of selecting an optimal train-test split ratio that considers tradeoffs such as model performance metrics, statistical measures, and resource constraints. Ultimately, these insights contribute to a deeper understanding of how ratio selection impacts the effectiveness and reliability of machine learning applications across diverse fields.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.