Prediction of Liver Cancer Based on DNA Sequence Using Ensemble Method

Lailil Muflikhah; Nashi Widodo; Wayan Firdaus Mahmudy

doi:10.1109/ISRITI51436.2020.9315341

Prediction of Liver Cancer Based on DNA Sequence Using Ensemble Method

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Chronic hepatitis B virus (HBV) infection is strongly associated with liver cancer. The DNA sequence of the virus is integrated into the human genome and affected the cell cycle. HBx is a virus gene that is responsible to replicate for survival even though it has a high mutation rate. Machine learning methods are an effective way in biological analysis and are widely used in diagnosis to make a prediction. This study is addressed to predict liver cancer using a machine learning method based on the DNA sequence of HBV. However, unbalanced data impacts the performance evaluation of the learning method, especially for sensitivity and specificity. Therefore, this paper is proposed the ensemble method to improve the performance of prediction. We compare several classifier methods including Naive Bayes, GLM, KNN, SVM, and C5.0 Decision Tree. The results show that the ensemble method achieves a high evaluation performance value with an accuracy rate of 88.4%, a sensitivity rate of 88.4%, and a specificity rate of 91.4%.

Original language	English
Title of host publication	2020 3rd International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2020
Editors	Ferry Wahyu Wibowo
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	37-41
Number of pages	5
ISBN (Electronic)	9781728184067
DOIs	https://doi.org/10.1109/ISRITI51436.2020.9315341
Publication status	Published - 10 Dec 2020
Event	3rd International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2020 - Yogyakarta, Indonesia Duration: 10 Dec 2020 → …

Publication series

Name	2020 3rd International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2020

Conference

Conference	3rd International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2020
Country/Territory	Indonesia
City	Yogyakarta
Period	10/12/20 → …

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

an ensemble method
DNA sequence
HBx
liver cancer
prediction

Access to Document

10.1109/ISRITI51436.2020.9315341

Cite this

Muflikhah, L., Widodo, N., Mahmudy, W. F., & Solimun (2020). Prediction of Liver Cancer Based on DNA Sequence Using Ensemble Method. In F. W. Wibowo (Ed.), 2020 3rd International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2020 (pp. 37-41). Article 9315341 (2020 3rd International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISRITI51436.2020.9315341

Muflikhah, Lailil ; Widodo, Nashi ; Mahmudy, Wayan Firdaus et al. / Prediction of Liver Cancer Based on DNA Sequence Using Ensemble Method. 2020 3rd International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2020. editor / Ferry Wahyu Wibowo. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 37-41 (2020 3rd International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2020).

@inproceedings{01b91c9c1610420a9d11a2f89580deae,

title = "Prediction of Liver Cancer Based on DNA Sequence Using Ensemble Method",

abstract = "Chronic hepatitis B virus (HBV) infection is strongly associated with liver cancer. The DNA sequence of the virus is integrated into the human genome and affected the cell cycle. HBx is a virus gene that is responsible to replicate for survival even though it has a high mutation rate. Machine learning methods are an effective way in biological analysis and are widely used in diagnosis to make a prediction. This study is addressed to predict liver cancer using a machine learning method based on the DNA sequence of HBV. However, unbalanced data impacts the performance evaluation of the learning method, especially for sensitivity and specificity. Therefore, this paper is proposed the ensemble method to improve the performance of prediction. We compare several classifier methods including Naive Bayes, GLM, KNN, SVM, and C5.0 Decision Tree. The results show that the ensemble method achieves a high evaluation performance value with an accuracy rate of 88.4\%, a sensitivity rate of 88.4\%, and a specificity rate of 91.4\%.",

keywords = "an ensemble method, DNA sequence, HBx, liver cancer, prediction",

author = "Lailil Muflikhah and Nashi Widodo and Mahmudy, \{Wayan Firdaus\} and Solimun",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 3rd International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2020 ; Conference date: 10-12-2020",

year = "2020",

month = dec,

day = "10",

doi = "10.1109/ISRITI51436.2020.9315341",

language = "English",

series = "2020 3rd International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2020",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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}

Muflikhah, L , Widodo, N , Mahmudy, WF & Solimun 2020, Prediction of Liver Cancer Based on DNA Sequence Using Ensemble Method. in FW Wibowo (ed.), 2020 3rd International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2020., 9315341, 2020 3rd International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2020, Institute of Electrical and Electronics Engineers Inc., pp. 37-41, 3rd International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2020, Yogyakarta, Indonesia, 10/12/20. https://doi.org/10.1109/ISRITI51436.2020.9315341

Prediction of Liver Cancer Based on DNA Sequence Using Ensemble Method. / Muflikhah, Lailil ; Widodo, Nashi ; Mahmudy, Wayan Firdaus et al.
2020 3rd International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2020. ed. / Ferry Wahyu Wibowo. Institute of Electrical and Electronics Engineers Inc., 2020. p. 37-41 9315341 (2020 3rd International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Prediction of Liver Cancer Based on DNA Sequence Using Ensemble Method

AU - Muflikhah, Lailil

AU - Widodo, Nashi

AU - Mahmudy, Wayan Firdaus

AU - Solimun,

PY - 2020/12/10

Y1 - 2020/12/10

N2 - Chronic hepatitis B virus (HBV) infection is strongly associated with liver cancer. The DNA sequence of the virus is integrated into the human genome and affected the cell cycle. HBx is a virus gene that is responsible to replicate for survival even though it has a high mutation rate. Machine learning methods are an effective way in biological analysis and are widely used in diagnosis to make a prediction. This study is addressed to predict liver cancer using a machine learning method based on the DNA sequence of HBV. However, unbalanced data impacts the performance evaluation of the learning method, especially for sensitivity and specificity. Therefore, this paper is proposed the ensemble method to improve the performance of prediction. We compare several classifier methods including Naive Bayes, GLM, KNN, SVM, and C5.0 Decision Tree. The results show that the ensemble method achieves a high evaluation performance value with an accuracy rate of 88.4%, a sensitivity rate of 88.4%, and a specificity rate of 91.4%.

AB - Chronic hepatitis B virus (HBV) infection is strongly associated with liver cancer. The DNA sequence of the virus is integrated into the human genome and affected the cell cycle. HBx is a virus gene that is responsible to replicate for survival even though it has a high mutation rate. Machine learning methods are an effective way in biological analysis and are widely used in diagnosis to make a prediction. This study is addressed to predict liver cancer using a machine learning method based on the DNA sequence of HBV. However, unbalanced data impacts the performance evaluation of the learning method, especially for sensitivity and specificity. Therefore, this paper is proposed the ensemble method to improve the performance of prediction. We compare several classifier methods including Naive Bayes, GLM, KNN, SVM, and C5.0 Decision Tree. The results show that the ensemble method achieves a high evaluation performance value with an accuracy rate of 88.4%, a sensitivity rate of 88.4%, and a specificity rate of 91.4%.

KW - an ensemble method

KW - DNA sequence

KW - HBx

KW - liver cancer

KW - prediction

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DO - 10.1109/ISRITI51436.2020.9315341

M3 - Conference contribution

AN - SCOPUS:85100014381

T3 - 2020 3rd International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2020

SP - 37

EP - 41

BT - 2020 3rd International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2020

A2 - Wibowo, Ferry Wahyu

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 3rd International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2020

Y2 - 10 December 2020

ER -

Muflikhah L , Widodo N , Mahmudy WF , Solimun. Prediction of Liver Cancer Based on DNA Sequence Using Ensemble Method. In Wibowo FW, editor, 2020 3rd International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 37-41. 9315341. (2020 3rd International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2020). doi: 10.1109/ISRITI51436.2020.9315341

Prediction of Liver Cancer Based on DNA Sequence Using Ensemble Method

Abstract

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UN SDGs

Keywords

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