Optimal cervical cancer classification using Gauss-Newton representation based algorithm

Z. Rustam, V. A.W. Hapsari, M. R. Solihin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Cervical cancer is a cancer that associated with infection with Human Papillomavirus (HPV). It occurs when the cells grow abnormally on the lowest portion of the woman's uterus. Most precancerous cervical cancer cases are on 20s or 30s women, but most cases diagnosed on 50s women. Cervical cancer has slow progression, it is one of the most preventable cancer and can be prevented by effective treatment. So, timely detection is important to know what effective treatment we can use. Machine learning techniques are being largely used to classify cervical cancer because of its precision and its capability to diagnose efficiently. In this paper, to identify the classification of cervical cancer, we have proposed a Gauss Newton representation-based algorithm (GNRBA) methods. Sparse representation is used with training sample selection, and Euclidean distance measure is used in the subset selection. The proposed method is less complexity, as compared to traditional representation method, more effective and easier to implement. This GNBRA method is examined on the cervical cancer database from Kaggle dataset repository. We used Stratified Shuffle Split to split the data. The experimental results show that the proposed GNBRA method give the 93 % accuracy. So, GNBRA method could be an ideal alternative for helping the clinical experts to classify cervical cancer.

Original languageEnglish
Title of host publicationProceedings of the 4th International Symposium on Current Progress in Mathematics and Sciences, ISCPMS 2018
EditorsTerry Mart, Djoko Triyono, Ivandini T. Anggraningrum
PublisherAmerican Institute of Physics Inc.
ISBN (Electronic)9780735419155
DOIs
Publication statusPublished - 4 Nov 2019
Event4th International Symposium on Current Progress in Mathematics and Sciences 2018, ISCPMS 2018 - Depok, Indonesia
Duration: 30 Oct 201831 Oct 2018

Publication series

NameAIP Conference Proceedings
Volume2168
ISSN (Print)0094-243X
ISSN (Electronic)1551-7616

Conference

Conference4th International Symposium on Current Progress in Mathematics and Sciences 2018, ISCPMS 2018
CountryIndonesia
CityDepok
Period30/10/1831/10/18

Keywords

  • cervical cancer classification
  • Euclidean
  • Gauss-Newton

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  • Cite this

    Rustam, Z., Hapsari, V. A. W., & Solihin, M. R. (2019). Optimal cervical cancer classification using Gauss-Newton representation based algorithm. In T. Mart, D. Triyono, & I. T. Anggraningrum (Eds.), Proceedings of the 4th International Symposium on Current Progress in Mathematics and Sciences, ISCPMS 2018 [020045] (AIP Conference Proceedings; Vol. 2168). American Institute of Physics Inc.. https://doi.org/10.1063/1.5132472