ON COMPUTATIONAL BAYESIAN ORDINAL LOGISTIC REGRESSION LINK FUNCTION IN CASES OF CERVICAL CANCER IN TUBAN

  • Nur Mahmudah Nahdlatul Ulama Sunan Giri University
  • Fetrika Anggraini Nahdlatul Ulama University
DOI: https://doi.org/10.30598/barekengvol16iss3pp909-918
Keywords: Bayesian, Gibbs Sampling, Kanker Serviks, MCMC, Regresi Logistik ordinal

Abstract

Cervical cancer is the most common cancer that causes death in women. This cancer is mainly caused by Human Papilloma Virus (HPV). It is estimated that 52 million of Indonesian women are at risk of having cancer, and 36% of female cancer patients suffer from cervical cancer. This type of cancer cannot be diagnosed immediately as there is several years of pre-malignancy phase; thus, early detection or screening is needed to prevent it from turning into malignant. Pap test as a screening program can detect cancer, precancer, and normal condition. To understand the predicting factors of the test results, a comprehensive mathematical modelling was created using the link function of Bayesian Ordinal Logistic Regression. This study observed several possible factors that may affect Pap test results in Tuban regency, namely Age (X1), Education (X2), Childbirth Experience (X3), Use of Contraceptives (X4), Menstrual Cycle (X5), Age of First Menstruation (X6), History of Miscarriage (X7), Anemia (X8) and Number of Sexual Partners (X9) . The outcomes indicated that the predicting factors of Pap cervical cancer results are Age (X1), Education (X2), Childbirth Experience (X3), Use of Contraceptives (X4), Menstrual Cycle (X5), and Anemia (X8). In this model, there is an inexplainable error dependency as indicated by the varied constance values of alpha.

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Published
2022-09-01
How to Cite
[1]
N. Mahmudah and F. Anggraini, “ON COMPUTATIONAL BAYESIAN ORDINAL LOGISTIC REGRESSION LINK FUNCTION IN CASES OF CERVICAL CANCER IN TUBAN”, BAREKENG: J. Math. & App., vol. 16, no. 3, pp. 909-918, Sep. 2022.
Issue
Vol 16 No 3 (2022): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Section
Articles

Copyright (c) 2022 Nur - Mahmudah, Fetrika Anggraini

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