ArticleBriefings in bioinformatics2024
A novel deep machine learning algorithm with dimensionality and size reduction approaches for feature elimination: thyroid cancer diagnoses with randomly missing data.
Article in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Accuracy of Radiomics-Based Machine Learning for Predicting Risk of Recurrence in Non-Small Cell Lung Cancer: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Continuous Temperature Monitoring via Wearable Devices for Fever or Infection Management in Acute Care Hospitals: Scoping Review of Clinical Implementation and Data Analytics.JMIR mHealth and uHealth · 2026Article
- Prediction of Clinically Significant Depressive Symptoms at 2-Year Follow-Up in Older Adults: Machine Learning Study Using the English Longitudinal Study of Ageing.JMIR formative research · 2026Article
- Hepatic Steatosis Severity Prediction in Nonobese Individuals: Machine Learning Model Development and Validation.Journal of medical Internet research · 2026Article
- Risk stratification for reoperation outcomes in recurrent or persistent papillary thyroid carcinoma: development and validation of a machine learning model.BMC medical informatics and decision making · 2026Article
- Knowledge-Guided Explainable Recommendation Tool for Cancer Risk Prediction Models Using Retrieval-Augmented Large Language Models: Development and Validation Study.JMIR medical informatics · 2026Article
- Integrating Deep Learning and Radiomics in Differentiating Papillary Thyroid Microcarcinoma from Papillary Thyroid Carcinoma with Ultrasound Images.Cancer management and research · 2025Article
- Incremental modelling and analysis of biological systems with fuzzy hybrid Petri nets.Briefings in bioinformatics · 2024Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Thyroid cancer incidences endure to increase even though a large number of inspection tools have been developed recently. Since there is no standard and certain procedure to follow for the thyroid cancer diagnoses, clinicians require conducting various tests. This scrutiny process yields multi-dimensional big data and lack of a common approach leads to randomly distributed missing (sparse) data, which are both formidable challenges for the machine learning algorithms. This paper aims to develop an accurate and computationally efficient deep learning algorithm to diagnose the thyroid cancer. In this respect, randomly distributed missing data stemmed singularity in learning problems is treated and dimensionality reduction with inner and target similarity approaches are developed to select the most informative input datasets. In addition, size reduction with the hierarchical clustering algorithm is performed to eliminate the considerably similar data samples. Four machine learning algorithms are trained and also tested with the unseen data to validate their generalization and robustness abilities. The results yield 100% training and 83% testing preciseness for the unseen data. Computational time efficiencies of the algorithms are also examined under the equal conditions.
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