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Updated: Apr 9, 2026

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

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Published on: April 14, 2023

Oral Cancer Detection By Using Tabular Data Synthesis and Classification.

Zhiyun Xue¹, Sivaramakrishnan Rajaraman¹, Zhaohui Liang¹

¹Division of Intramural Research, National Library of Medicine National Institutes of Health, Bethesda, MD, USA.

Proceedings ... ICDM Workshops. IEEE International Conference on Data Mining

|April 8, 2026

Summary

This summary is machine-generated.

This study introduces an AI method using tabular clinical data for oral cancer screening, distinguishing it from precancer. Synthetic data balancing significantly improved classification performance, showing promise for AI prediction tasks.

Keywords:

data balancing deep learning oral cancer screening tabular data classification tabular data synthesis

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Area of Science:

Artificial Intelligence
Oncology
Data Science

Background:

Oral cancer screening relies on various data types, including clinical information.
Distinguishing oral precancer from oral cancer is crucial for timely intervention.
Clinical data imbalance poses a challenge for developing accurate AI screening models.

Purpose of the Study:

To develop an AI-driven method for oral cancer screening using non-image clinical data.
To improve classification performance by addressing data imbalance in clinical datasets.
To evaluate the role of tabular clinical data in differentiating oral cancer from precancer.

Main Methods:

Utilized deep learning techniques for classification of tabular clinical data.
Implemented tabular data synthesis to balance imbalanced datasets.
Conducted extensive experiments with multiple datasets and models to evaluate performance.

Main Results:

Achieved a Youden index of ~0.74, balanced accuracy of ~0.83, and sensitivity of ~0.90.
Demonstrated statistically significant improvements (p < 0.05) in key metrics using synthetic data.
Confirmed the importance of tabular clinical information for oral cancer detection using AI.

Conclusions:

AI analysis of tabular clinical data is effective for oral cancer screening.
Synthetic data generation is a viable strategy to enhance AI model performance in oncology.
This approach offers a promising direction for future clinical AI prediction tasks.