ArticleBMC bioinformatics2024
Deep self-supervised machine learning algorithms with a novel feature elimination and selection approaches for blood test-based multi-dimensional health risks classification.
Article in BMC bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Hepatic Steatosis Severity Prediction in Nonobese Individuals: Machine Learning Model Development and Validation.Journal of medical Internet research · 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
- Application of XGBoost and logistic regression in predicting 90 days mortality for elderly severe acute renal failure patients.Scientific reports · 2026Article
- Identification and cause analysis on unplanned reoperations by text classification approach.Scientific reports · 2025Article
- Interpretable manifold learning for T-wave alternans assessment with electrocardiographic imaging.Engineering applications of artificial intelligence · 2025Article
- 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
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Authors and funding
2 authors.
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Abstract
backgroundBlood test is extensively performed for screening, diagnoses and surveillance purposes. Although it is possible to automatically evaluate the raw blood test data with the advanced deep self-supervised machine learning approaches, it has not been profoundly investigated and implemented yet.
resultsThis paper proposes deep machine learning algorithms with multi-dimensional adaptive feature elimination, self-feature weighting and novel feature selection approaches. To classify the health risks based on the processed data with the deep layers, four machine learning algorithms having various properties from being utterly model free to gradient driven are modified.
conclusionsThe results show that the proposed deep machine learning algorithms can remove the unnecessary features, assign self-importance weights, selects their most informative ones and classify the health risks automatically from the worst-case low to worst-case high values.
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