Hepatitis Detection using Random Forest based on SVM-RFE (Recursive Feature Elimination) Feature Selection and SMOTE

Rifky Yunus Krisnabayu; Achmad Ridok; Agung Setia Budi

doi:10.1145/3479645.3479668

Hepatitis Detection using Random Forest based on SVM-RFE (Recursive Feature Elimination) Feature Selection and SMOTE

Rifky Yunus Krisnabayu
, Achmad Ridok
, Agung Setia Budi

Faculty of Computer Science

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

5 Citations (Scopus)

Abstract

Hepatitis is a dangerous disease because it is a contagious disease and it is not easy to diagnose the disease early. Due to the difficulty of making an early diagnosis, the disease has the potential to become even more severe and increase the mortality rate. Therefore, it is necessary to develop predictive methods that can be used for the early detection of this disease. In this study, a hepatitis prediction method was developed using a random forest (RF) algorithm combined with feature selection using SVM-RFE (recursive feature elimination). Then, because the dataset used does not have a balanced distribution between classes, which is only 20% for the minority class, SMOTE (synthetic minority oversampling technique) is used to deal with this problem. To determine the best parameters in the model, Grid-Search is used as the tuning hyper-parameters. The classifier built with this approach produces 0.879 accuracy, 0.902 precision, and 0.966 ROC performance. This classifier proved to be better than the other classifiers.

Original language	English
Title of host publication	Proceedings of 2021 International Conference on Sustainable Information Engineering and Technology, SIET 2021
Publisher	Association for Computing Machinery
Pages	151-156
Number of pages	6
ISBN (Electronic)	9781450384070
DOIs	https://doi.org/10.1145/3479645.3479668
Publication status	Published - 13 Sept 2021
Event	6th International Conference on Sustainable Information Engineering and Technology, SIET 2021 - Virtual, Online, Indonesia Duration: 13 Sept 2021 → 14 Sept 2021

Publication series

Name	ACM International Conference Proceeding Series

Conference

Conference	6th International Conference on Sustainable Information Engineering and Technology, SIET 2021
Country/Territory	Indonesia
City	Virtual, Online
Period	13/09/21 → 14/09/21

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

Hepatitis prediction
Random forest
Recursive feature elimination
Synthetic minority oversampling technique

Access to Document

10.1145/3479645.3479668

Cite this

Krisnabayu, R. Y., Ridok, A., & Budi, A. S. (2021). Hepatitis Detection using Random Forest based on SVM-RFE (Recursive Feature Elimination) Feature Selection and SMOTE. In Proceedings of 2021 International Conference on Sustainable Information Engineering and Technology, SIET 2021 (pp. 151-156). (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3479645.3479668

Krisnabayu, Rifky Yunus ; Ridok, Achmad ; Budi, Agung Setia. / Hepatitis Detection using Random Forest based on SVM-RFE (Recursive Feature Elimination) Feature Selection and SMOTE. Proceedings of 2021 International Conference on Sustainable Information Engineering and Technology, SIET 2021. Association for Computing Machinery, 2021. pp. 151-156 (ACM International Conference Proceeding Series).

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title = "Hepatitis Detection using Random Forest based on SVM-RFE (Recursive Feature Elimination) Feature Selection and SMOTE",

abstract = "Hepatitis is a dangerous disease because it is a contagious disease and it is not easy to diagnose the disease early. Due to the difficulty of making an early diagnosis, the disease has the potential to become even more severe and increase the mortality rate. Therefore, it is necessary to develop predictive methods that can be used for the early detection of this disease. In this study, a hepatitis prediction method was developed using a random forest (RF) algorithm combined with feature selection using SVM-RFE (recursive feature elimination). Then, because the dataset used does not have a balanced distribution between classes, which is only 20\% for the minority class, SMOTE (synthetic minority oversampling technique) is used to deal with this problem. To determine the best parameters in the model, Grid-Search is used as the tuning hyper-parameters. The classifier built with this approach produces 0.879 accuracy, 0.902 precision, and 0.966 ROC performance. This classifier proved to be better than the other classifiers.",

keywords = "Hepatitis prediction, Random forest, Recursive feature elimination, Synthetic minority oversampling technique",

author = "Krisnabayu, \{Rifky Yunus\} and Achmad Ridok and Budi, \{Agung Setia\}",

note = "Publisher Copyright: {\textcopyright} 2021 ACM.; 6th International Conference on Sustainable Information Engineering and Technology, SIET 2021 ; Conference date: 13-09-2021 Through 14-09-2021",

year = "2021",

month = sep,

day = "13",

doi = "10.1145/3479645.3479668",

language = "English",

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Krisnabayu, RY, Ridok, A & Budi, AS 2021, Hepatitis Detection using Random Forest based on SVM-RFE (Recursive Feature Elimination) Feature Selection and SMOTE. in Proceedings of 2021 International Conference on Sustainable Information Engineering and Technology, SIET 2021. ACM International Conference Proceeding Series, Association for Computing Machinery, pp. 151-156, 6th International Conference on Sustainable Information Engineering and Technology, SIET 2021, Virtual, Online, Indonesia, 13/09/21. https://doi.org/10.1145/3479645.3479668

Hepatitis Detection using Random Forest based on SVM-RFE (Recursive Feature Elimination) Feature Selection and SMOTE. / Krisnabayu, Rifky Yunus; Ridok, Achmad; Budi, Agung Setia.
Proceedings of 2021 International Conference on Sustainable Information Engineering and Technology, SIET 2021. Association for Computing Machinery, 2021. p. 151-156 (ACM International Conference Proceeding Series).

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

TY - GEN

T1 - Hepatitis Detection using Random Forest based on SVM-RFE (Recursive Feature Elimination) Feature Selection and SMOTE

AU - Krisnabayu, Rifky Yunus

AU - Ridok, Achmad

AU - Budi, Agung Setia

PY - 2021/9/13

Y1 - 2021/9/13

N2 - Hepatitis is a dangerous disease because it is a contagious disease and it is not easy to diagnose the disease early. Due to the difficulty of making an early diagnosis, the disease has the potential to become even more severe and increase the mortality rate. Therefore, it is necessary to develop predictive methods that can be used for the early detection of this disease. In this study, a hepatitis prediction method was developed using a random forest (RF) algorithm combined with feature selection using SVM-RFE (recursive feature elimination). Then, because the dataset used does not have a balanced distribution between classes, which is only 20% for the minority class, SMOTE (synthetic minority oversampling technique) is used to deal with this problem. To determine the best parameters in the model, Grid-Search is used as the tuning hyper-parameters. The classifier built with this approach produces 0.879 accuracy, 0.902 precision, and 0.966 ROC performance. This classifier proved to be better than the other classifiers.

AB - Hepatitis is a dangerous disease because it is a contagious disease and it is not easy to diagnose the disease early. Due to the difficulty of making an early diagnosis, the disease has the potential to become even more severe and increase the mortality rate. Therefore, it is necessary to develop predictive methods that can be used for the early detection of this disease. In this study, a hepatitis prediction method was developed using a random forest (RF) algorithm combined with feature selection using SVM-RFE (recursive feature elimination). Then, because the dataset used does not have a balanced distribution between classes, which is only 20% for the minority class, SMOTE (synthetic minority oversampling technique) is used to deal with this problem. To determine the best parameters in the model, Grid-Search is used as the tuning hyper-parameters. The classifier built with this approach produces 0.879 accuracy, 0.902 precision, and 0.966 ROC performance. This classifier proved to be better than the other classifiers.

KW - Hepatitis prediction

KW - Random forest

KW - Recursive feature elimination

KW - Synthetic minority oversampling technique

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DO - 10.1145/3479645.3479668

M3 - Conference contribution

AN - SCOPUS:85118868558

T3 - ACM International Conference Proceeding Series

SP - 151

EP - 156

BT - Proceedings of 2021 International Conference on Sustainable Information Engineering and Technology, SIET 2021

PB - Association for Computing Machinery

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Y2 - 13 September 2021 through 14 September 2021

ER -

Krisnabayu RY, Ridok A, Budi AS. Hepatitis Detection using Random Forest based on SVM-RFE (Recursive Feature Elimination) Feature Selection and SMOTE. In Proceedings of 2021 International Conference on Sustainable Information Engineering and Technology, SIET 2021. Association for Computing Machinery. 2021. p. 151-156. (ACM International Conference Proceeding Series). doi: 10.1145/3479645.3479668

Hepatitis Detection using Random Forest based on SVM-RFE (Recursive Feature Elimination) Feature Selection and SMOTE

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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