Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2024 Apr 18;13(8):2363.
doi: 10.3390/jcm13082363.

Machine Learning Prediction of Tongue Pressure in Elderly Patients with Head and Neck Tumor: A Cross-Sectional Study

Xuewei Han  1 , Ziyi Bai  1 , Kaoru Mogushi  2 , Takeshi Hase  2   3   4   5 , Katsuyuki Takeuchi  2 , Yoritsugu Iida  2 , Yuka I Sumita  1   6 , Noriyuki Wakabayashi  1
Affiliations

Machine Learning Prediction of Tongue Pressure in Elderly Patients with Head and Neck Tumor: A Cross-Sectional Study

Xuewei Han et al. J Clin Med. .

Abstract

Background: This investigation sought to cross validate the predictors of tongue pressure recovery in elderly patients' post-treatment for head and neck tumors, leveraging advanced machine learning techniques. Methods: By employing logistic regression, support vector regression, random forest, and extreme gradient boosting, the study analyzed an array of variables including patient demographics, surgery types, dental health status, and age, drawn from comprehensive medical records and direct tongue pressure assessments. Results: Among the models, logistic regression emerged as the most effective, demonstrating an accuracy of 0.630 [95% confidence interval (CI): 0.370-0.778], F1 score of 0.688 [95% confidence interval (CI): 0.435-0.853], precision of 0.611 [95% confidence interval (CI): 0.313-0.801], recall of 0.786 [95% confidence interval (CI): 0.413-0.938] and an area under the receiver operating characteristic curve of 0.626 [95% confidence interval (CI): 0.409-0.806]. This model distinctly highlighted the significance of glossectomy (p = 0.039), the presence of functional teeth (p = 0.043), and the patient's age (p = 0.044) as pivotal factors influencing tongue pressure, setting the threshold for statistical significance at p < 0.05. Conclusions: The analysis underscored the critical role of glossectomy, the presence of functional natural teeth, and age as determinants of tongue pressure in logistics regression, with the presence of natural teeth and the tumor site located in the tongue consistently emerging as the key predictors across all computational models employed in this study.

Keywords: XGBoost; head and neck tumor; logistic regression; machine learning; oral function; random forest; support vector machine; tongue pressure.

PubMed Disclaimer

Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Tongue-pressure-measuring instrument equipped with a balloon probe. Maximum tongue pressure (kPa) is displayed in box 1, and current tongue pressure (kPa) is displayed in box 2.
Figure 2
Figure 2
Receiver operating characteristic (ROC) curve of multiple logistic regression in testing dataset.
See this image and copyright information in PMC

Similar articles

See all similar articles

References

    1. Yoshida M., Hiraoka A., Takeda C., Mori T., Maruyama M., Yoshikawa M., Tsuga K. Oral Hypofunction and Its Relation to Frailty and Sarcopenia in Community-Dwelling Older People. Gerodontology. 2022;39:26–32. doi: 10.1111/ger.12603. - DOI - PubMed
    1. Morimata J., Otomaru T., Murase M., Haraguchi M., Sumita Y., Taniguchi H. Investigation of Factor Affecting Health-Related Quality of Life in Head and Neck Cancer Patients. Gerodontology. 2013;30:194–200. doi: 10.1111/j.1741-2358.2012.00662.x. - DOI - PubMed
    1. Yanagi A., Murase M., Sumita Y.I., Taniguchi H. Investigation of Nutritional Status Using the Mini Nutritional Assessment-Short Form and Analysis of the Relevant Factors in Patients with Head and Neck Tumour. Gerodontology. 2017;34:227–231. doi: 10.1111/ger.12253. - DOI - PubMed
    1. Minakuchi S., Tsuga K., Ikebe K., Ueda T., Tamura F., Nagao K., Furuya J., Matsuo K., Yamamoto K., Kanazawa M., et al. Oral Hypofunction in the Older Population: Position Paper of the Japanese Society of Gerodontology in 2016. Gerodontology. 2018;35:317–324. doi: 10.1111/ger.12347. - DOI - PubMed
    1. Izumi M., Akifusa S. Tongue Cleaning in the Elderly and Its Role in the Respiratory and Swallowing Functions: Benefits and Medical Perspectives. J. Oral. Rehabil. 2021;48:1395–1403. doi: 10.1111/joor.13266. - DOI - PubMed

LinkOut - more resources