ArticleJournal of medical systems2015
The application of data mining techniques to oral cancer prognosis.
Article in Journal of medical systems, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prognosing post-treatment outcomes of head and neck cancer using structured data and machine learning: A systematic review.PloS one · 2024Pooled it
- Performance of Machine Learning Models for Prognosis Prediction in Oral Cavity Squamous Cell Carcinoma: A Systematic Review.Cancers · 2026Review
- Artificial Intelligence in Head and Neck Surgery.Otolaryngologic clinics of North America · 2024Review
- Artificial intelligence-based predictive model for guidance on treatment strategy selection in oral and maxillofacial surgery.Heliyon · 2024Review
- Survival estimation of oral cancer using fuzzy deep learning.BMC oral health · 2024Article
- A survival prediction model via interpretable machine learning for patients with oropharyngeal cancer following radiotherapy.Journal of cancer research and clinical oncology · 2023Article
- Artificial Intelligence for Image Analysis in Oral Squamous Cell Carcinoma: A Review.Diagnostics (Basel, Switzerland) · 2023Review
- A Current Review of Machine Learning and Deep Learning Models in Oral Cancer Diagnosis: Recent Technologies, Open Challenges, and Future Research Directions.Diagnostics (Basel, Switzerland) · 2023Review
- Empirical comparison of routinely collected electronic health record data for head and neck cancer-specific survival in machine-learnt prognostic models.Head & neck · 2023Article
- Development and Validation of a Non-Invasive, Chairside Oral Cavity Cancer Risk Assessment Prototype Using Machine Learning Approach.Journal of personalized medicine · 2022Article
- Discovery of Knowledge in the Incidence of a Type of Lung Cancer for Patients through Data Mining Models.Computational intelligence and neuroscience · 2022Article
- Application and Performance of Artificial Intelligence Technology in Oral Cancer Diagnosis and Prediction of Prognosis: A Systematic Review.Diagnostics (Basel, Switzerland) · 2021Review
- Machine learning based tissue analysis reveals Brachyury has a diagnosis value in breast cancer.Bioscience reports · 2021Article
- An Image Recognition Framework for Oral Cancer Cells.Journal of healthcare engineering · 2021Article
- Deep Learning in Head and Neck Tumor Multiomics Diagnosis and Analysis: Review of the Literature.Frontiers in genetics · 2021Review
- Article
- Changes of serum miR-223-3p in patients with oral cancer treated with TPF regimen and the prognosis.Oncology letters · 2020Article
- MiR-155 targeting FoxO3a regulates oral cancer cell proliferation, apoptosis, and DDP resistance through targeting FoxO3a.Cancer biomarkers : section A of Disease markers · 2020Article
- Big data analytics for preventive medicine.Neural computing & applications · 2020Article
- Predictive Factors of Advanced Colonic Adenomas and Cancer Using Data Mining.Middle East journal of digestive diseases · 2019Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study adopted an integrated procedure that combines the clustering and classification features of data mining technology to determine the differences between the symptoms shown in past cases where patients died from or survived oral cancer. Two data mining tools, namely decision tree and artificial neural network, were used to analyze the historical cases of oral cancer, and their performance was compared with that of logistic regression, the popular statistical analysis tool. Both decision tree and artificial neural network models showed superiority to the traditional statistical model. However, as to clinician, the trees created by the decision tree models are relatively easier to interpret compared to that of the artificial neural network models. Cluster analysis also discovers that those stage 4 patients whose also possess the following four characteristics are having an extremely low survival rate: pN is N2b, level of RLNM is level I-III, AJCC-T is T4, and cells mutate situation (G) is moderate.
Indexed as
Identifiers
25796587What OpenQuestion holds
Registered trials
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.