Machine Learning Approach for Thyroid Cancer Diagnosis Using Clinical Data

İpek Balıkçı Çiçek; Zeynep Küçükakçalı

doi:10.19127/mbsjohs.1282265

EN

Machine Learning Approach for Thyroid Cancer Diagnosis Using Clinical Data

Abstract

Objective: With an early diagnosis of thyroid cancer, one of the world's most significant health issues, it is feasible to treat the nodules before the spread of malignant thyroid gland cells. It has become crucial to develop models for predicting thyroid cancer. In light of this, the purpose of this study is to develop a clinical decision support model using the Bagged CART model, a machine learning (ML) model for the prediction of thyroid cancer. Methods: Between 2010 and 2012, 724 patients who applied to China Median University Shengjing Hospital comprised the study's data set. The dataset comprises information on nodule malignancies, demographic characteristics, ultrasound characteristics, and blood test results for all patients who underwent thyroidectomy. Using this open-access data set, the Bagged CART modeling technique was applied. Negative predictive value (NPV), specificity (Spe), balanced accuracy (BACC), positive predictive value (PPV), accuracy (ACC), sensitivity (Sen), and F1-score performance metrics were used to evaluate the model's predictive performance. In addition, a 10-fold cross-validation method was used to determine the validity of the model. In addition, variable importance was established, which reveals how much the input variables impact the output variable. Results: ACC, BACC, Sen, Spe, PPV, NPV, and F1-score obtained from the model performance metrics were calculated to 99.1%, 98.7%, 99.7%, 97.7%, 99.1%, 99.2%, and 99.4%, respectively, as a result of modeling. According to the variable importance values that were acquired for the input variables in the dataset that was investigated in this study, the seven variable that hold the greatest significance are as follows: size, TSH, blood flow: size, TSH, blood flow: enriched, multilateral: yes, FT4, site: isthmus, and age, in that order. Conclusion: As a result, the Bagged CART model was found to be effective at predicting thyroid cancer based on the findings of this study. In addition, in this study, risk factors for thyroid cancer were evaluated and their importance values were given. With these results, the decision-making process about the disease will be able to accelerate and thus, it will be able to effective in preventive medicine practices.

Keywords

Bagged CART, machine learning, thyroid cancer, risk factors, classification.

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Details

Primary Language

English

Subjects

Health Care Administration

Journal Section

Research Article

Authors

İpek Balıkçı Çiçek ^*
0000-0002-3805-9214
Türkiye

Zeynep Küçükakçalı
0000-0001-7956-9272
Türkiye

Publication Date

August 31, 2023

Submission Date

April 13, 2023

Acceptance Date

July 16, 2023

Published in Issue

Year 2023 Volume: 9 Number: 3

DOI

https://doi.org/10.19127/mbsjohs.1282265

IZ

https://izlik.org/JA77MM45MR

APA

Balıkçı Çiçek, İ., & Küçükakçalı, Z. (2023). Machine Learning Approach for Thyroid Cancer Diagnosis Using Clinical Data. Middle Black Sea Journal of Health Science, 9(3), 440-452. https://doi.org/10.19127/mbsjohs.1282265

AMA

1.Balıkçı Çiçek İ, Küçükakçalı Z. Machine Learning Approach for Thyroid Cancer Diagnosis Using Clinical Data. Mid Blac Sea J Health Sci. 2023;9(3):440-452. doi:10.19127/mbsjohs.1282265

Chicago

Balıkçı Çiçek, İpek, and Zeynep Küçükakçalı. 2023. “Machine Learning Approach for Thyroid Cancer Diagnosis Using Clinical Data”. Middle Black Sea Journal of Health Science 9 (3): 440-52. https://doi.org/10.19127/mbsjohs.1282265.

EndNote

Balıkçı Çiçek İ, Küçükakçalı Z (August 1, 2023) Machine Learning Approach for Thyroid Cancer Diagnosis Using Clinical Data. Middle Black Sea Journal of Health Science 9 3 440–452.

IEEE

[1]İ. Balıkçı Çiçek and Z. Küçükakçalı, “Machine Learning Approach for Thyroid Cancer Diagnosis Using Clinical Data”, Mid Blac Sea J Health Sci, vol. 9, no. 3, pp. 440–452, Aug. 2023, doi: 10.19127/mbsjohs.1282265.

ISNAD

Balıkçı Çiçek, İpek - Küçükakçalı, Zeynep. “Machine Learning Approach for Thyroid Cancer Diagnosis Using Clinical Data”. Middle Black Sea Journal of Health Science 9/3 (August 1, 2023): 440-452. https://doi.org/10.19127/mbsjohs.1282265.

JAMA

1.Balıkçı Çiçek İ, Küçükakçalı Z. Machine Learning Approach for Thyroid Cancer Diagnosis Using Clinical Data. Mid Blac Sea J Health Sci. 2023;9:440–452.

MLA

Balıkçı Çiçek, İpek, and Zeynep Küçükakçalı. “Machine Learning Approach for Thyroid Cancer Diagnosis Using Clinical Data”. Middle Black Sea Journal of Health Science, vol. 9, no. 3, Aug. 2023, pp. 440-52, doi:10.19127/mbsjohs.1282265.

Vancouver

1.İpek Balıkçı Çiçek, Zeynep Küçükakçalı. Machine Learning Approach for Thyroid Cancer Diagnosis Using Clinical Data. Mid Blac Sea J Health Sci. 2023 Aug. 1;9(3):440-52. doi:10.19127/mbsjohs.1282265

Machine Learning Approach for Thyroid Cancer Diagnosis Using Clinical Data

Abstract

Machine Learning Approach for Thyroid Cancer Diagnosis Using Clinical Data

Abstract

Keywords

References

Details

Primary Language

Subjects

Journal Section

Authors

Publication Date

Submission Date

Acceptance Date

Published in Issue

DOI

IZ

Cite

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