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Article

An Interpretable Machine Learning Approach for Hepatitis B Diagnosis

by
George Obaido
1,*,
Blessing Ogbuokiri
2,*,
Theo G. Swart
3,
Nimibofa Ayawei
4,
Sydney Mambwe Kasongo
5,
Kehinde Aruleba
6,*,
Ibomoiye Domor Mienye
7,*,
Idowu Aruleba
7,
Williams Chukwu
8,
Fadekemi Osaye
9,
Oluwaseun F. Egbelowo
10,
Simelane Simphiwe
11 and
Ebenezer Esenogho
3
1
Center for Human-Compatible Artificial Intelligence (CHAI), Berkeley Institute for Data Science (BIDS), University of California, Berkeley, CA 94720, USA
2
Department of Mathematics and Statistics, York University, Toronto, ON M3J 1P3, Canada
3
Center for Telecommunications, Department of Electrical and Electronic Engineering Science, University of Johannesburg, Johannesburg 2006, South Africa
4
Department of Chemistry, Bayelsa Medical University, Yenagoa PMB 178, Nigeria
5
Department of Industrial Engineering, Faculty of Engineering, Stellenbosch University, Stellenbosch 7600, South Africa
6
School of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK
7
Department of Electrical and Electronic Engineering Science, University of Johannesburg, Johannesburg 2006, South Africa
8
Department of Mathematics, Wake Forest University, Winston-Salem, NC 27109, USA
9
Department of Mathematics and Computer Science, Alabama State University, Montgomery, AL 36104, USA
10
Department of Integrative Biology, The University of Texas at Austin, Austin, TX 78712, USA
11
Department of Mathematics and Applied Mathematics, University of Johannesburg, Doornfontein 2028, South Africa
 
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*
Authors to whom correspondence should be addressed.
Appl. Sci. 2022, 12(21), 11127; https://doi.org/10.3390/app122111127
Submission received: 15 August 2022 / Revised: 18 October 2022 / Accepted: 31 October 2022 / Published: 2 November 2022
(This article belongs to the Special Issue Computational Intelligence in Bioinformatics and Computational Biology)
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Abstract

Hepatitis B is a potentially deadly liver infection caused by the hepatitis B virus. It is a serious public health problem globally. Substantial efforts have been made to apply machine learning in detecting the virus. However, the application of model interpretability is limited in the existing literature. Model interpretability makes it easier for humans to understand and trust the machine-learning model. Therefore, in this study, we used SHapley Additive exPlanations (SHAP), a game-based theoretical approach to explain and visualize the predictions of machine learning models applied for hepatitis B diagnosis. The algorithms used in building the models include decision tree, logistic regression, support vector machines, random forest, adaptive boosting (AdaBoost), and extreme gradient boosting (XGBoost), and they achieved balanced accuracies of 75%, 82%, 75%, 86%, 92%, and 90%, respectively. Meanwhile, the SHAP values showed that bilirubin is the most significant feature contributing to a higher mortality rate. Consequently, older patients are more likely to die with elevated bilirubin levels. The outcome of this study can aid health practitioners and health policymakers in explaining the result of machine learning models for health-related problems.
Keywords: disease prediction; hepatitis B; interpretability; machine learning disease prediction; hepatitis B; interpretability; machine learning

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MDPI and ACS Style

Obaido, G.; Ogbuokiri, B.; Swart, T.G.; Ayawei, N.; Kasongo, S.M.; Aruleba, K.; Mienye, I.D.; Aruleba, I.; Chukwu, W.; Osaye, F.; et al. An Interpretable Machine Learning Approach for Hepatitis B Diagnosis. Appl. Sci. 2022, 12, 11127. https://doi.org/10.3390/app122111127

AMA Style

Obaido G, Ogbuokiri B, Swart TG, Ayawei N, Kasongo SM, Aruleba K, Mienye ID, Aruleba I, Chukwu W, Osaye F, et al. An Interpretable Machine Learning Approach for Hepatitis B Diagnosis. Applied Sciences. 2022; 12(21):11127. https://doi.org/10.3390/app122111127

Chicago/Turabian Style

Obaido, George, Blessing Ogbuokiri, Theo G. Swart, Nimibofa Ayawei, Sydney Mambwe Kasongo, Kehinde Aruleba, Ibomoiye Domor Mienye, Idowu Aruleba, Williams Chukwu, Fadekemi Osaye, and et al. 2022. "An Interpretable Machine Learning Approach for Hepatitis B Diagnosis" Applied Sciences 12, no. 21: 11127. https://doi.org/10.3390/app122111127

APA Style

Obaido, G., Ogbuokiri, B., Swart, T. G., Ayawei, N., Kasongo, S. M., Aruleba, K., Mienye, I. D., Aruleba, I., Chukwu, W., Osaye, F., Egbelowo, O. F., Simphiwe, S., & Esenogho, E. (2022). An Interpretable Machine Learning Approach for Hepatitis B Diagnosis. Applied Sciences, 12(21), 11127. https://doi.org/10.3390/app122111127

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