A Machine Learning Classification Model for Gastrointestinal Health in Cancer Survivors: Roles of Telomere Length and Social Determinants of Health

Han, Claire J.; Ning, Xia; Burd, Christin E.; Tounkara, Fode; Kalady, Matthew F.; Noonan, Anne M.; Von Ah, Diane

doi:10.3390/ijerph21121694

Open AccessArticle

A Machine Learning Classification Model for Gastrointestinal Health in Cancer Survivors: Roles of Telomere Length and Social Determinants of Health

by

Claire J. Han

^1,2,*

,

Xia Ning

^3,4,

Christin E. Burd

⁵,

Fode Tounkara

^2,6,

Matthew F. Kalady

⁷,

Anne M. Noonan

⁸

and

Diane Von Ah

^1,2

¹

Center for Healthy Aging, Self-Management and Complex Care, College of Nursing, The Ohio State University, Columbus, OH 43210, USA

²

The James: Cancer Treatment and Research Center, The Ohio State University, Columbus, OH 43210, USA

³

Clinical Informatics and Implementation Science, Biomedical Informatics (BMI), College of Medicine, The Ohio State University, Columbus, OH 43210, USA

⁴

Computer Science and Engineering (CSE), College of Engineering, The Ohio State University, Columbus, OH 43210, USA

⁵

Departments of Molecular Genetics, Cancer Biology, and Genetics, The Ohio State University, Columbus, OH 43210, USA

⁶

Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH 43210, USA

⁷

Division of Colon and Rectal Surgery, Clinical Cancer Genetics Program, The James: Cancer Treatment and Research Center, The Ohio State University, Columbus, OH 43210, USA

⁸

GI Medical Oncology Section, The James: Cancer Treatment and Research Center, The Ohio State University, Columbus, OH 43210, USA

^*

Author to whom correspondence should be addressed.

Int. J. Environ. Res. Public Health 2024, 21(12), 1694; https://doi.org/10.3390/ijerph21121694

Submission received: 17 October 2024 / Revised: 14 December 2024 / Accepted: 16 December 2024 / Published: 19 December 2024

(This article belongs to the Special Issue Advancing Health Equity—Addressing Cancer Disparities)

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Abstract

Background: Gastrointestinal (GI) distress is prevalent and often persistent among cancer survivors, impacting their quality of life, nutrition, daily function, and mortality. GI health screening is crucial for preventing and managing this distress. However, accurate classification methods for GI health remain unexplored. We aimed to develop machine learning (ML) models to classify GI health status (better vs. worse) by incorporating biological aging and social determinants of health (SDOH) indicators in cancer survivors. Methods: We included 645 adult cancer survivors from the 1999–2002 NHANES survey. Using training and test datasets, we employed six ML models to classify GI health conditions (better vs. worse). These models incorporated leukocyte telomere length (TL), SDOH, and demographic/clinical data. Results: Among the ML models, the random forest (RF) performed the best, achieving a high area under the curve (AUC = 0.98) in the training dataset. The gradient boosting machine (GBM) demonstrated excellent classification performance with a high AUC (0.80) in the test dataset. TL, several socio-economic factors, cancer risk behaviors (including lifestyle choices), and inflammatory markers were associated with GI health. The most significant input features for better GI health in our ML models were longer TL and an annual household income above the poverty level, followed by routine physical activity, low white blood cell counts, and food security. Conclusions: Our findings provide valuable insights into classifying and identifying risk factors related to GI health, including biological aging and SDOH indicators. To enhance model predictability, further longitudinal studies and external clinical validations are necessary.

Keywords: cancer survivors; gastrointestinal health; telomere; social determinants of health; machine learning

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MDPI and ACS Style

Han, C.J.; Ning, X.; Burd, C.E.; Tounkara, F.; Kalady, M.F.; Noonan, A.M.; Von Ah, D. A Machine Learning Classification Model for Gastrointestinal Health in Cancer Survivors: Roles of Telomere Length and Social Determinants of Health. Int. J. Environ. Res. Public Health 2024, 21, 1694. https://doi.org/10.3390/ijerph21121694

AMA Style

Han CJ, Ning X, Burd CE, Tounkara F, Kalady MF, Noonan AM, Von Ah D. A Machine Learning Classification Model for Gastrointestinal Health in Cancer Survivors: Roles of Telomere Length and Social Determinants of Health. International Journal of Environmental Research and Public Health. 2024; 21(12):1694. https://doi.org/10.3390/ijerph21121694

Chicago/Turabian Style

Han, Claire J., Xia Ning, Christin E. Burd, Fode Tounkara, Matthew F. Kalady, Anne M. Noonan, and Diane Von Ah. 2024. "A Machine Learning Classification Model for Gastrointestinal Health in Cancer Survivors: Roles of Telomere Length and Social Determinants of Health" International Journal of Environmental Research and Public Health 21, no. 12: 1694. https://doi.org/10.3390/ijerph21121694

APA Style

Han, C. J., Ning, X., Burd, C. E., Tounkara, F., Kalady, M. F., Noonan, A. M., & Von Ah, D. (2024). A Machine Learning Classification Model for Gastrointestinal Health in Cancer Survivors: Roles of Telomere Length and Social Determinants of Health. International Journal of Environmental Research and Public Health, 21(12), 1694. https://doi.org/10.3390/ijerph21121694

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A Machine Learning Classification Model for Gastrointestinal Health in Cancer Survivors: Roles of Telomere Length and Social Determinants of Health

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