An Interpretable Machine Learning Approach for Hepatitis B Diagnosis
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 lite...
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| Format: | Article |
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MDPI AG
2022-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/21/11127 |
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| author | George Obaido Blessing Ogbuokiri Theo G. Swart Nimibofa Ayawei Sydney Mambwe Kasongo Kehinde Aruleba Ibomoiye Domor Mienye Idowu Aruleba Williams Chukwu Fadekemi Osaye Oluwaseun F. Egbelowo Simelane Simphiwe Ebenezer Esenogho |
| author_facet | George Obaido Blessing Ogbuokiri Theo G. Swart Nimibofa Ayawei Sydney Mambwe Kasongo Kehinde Aruleba Ibomoiye Domor Mienye Idowu Aruleba Williams Chukwu Fadekemi Osaye Oluwaseun F. Egbelowo Simelane Simphiwe Ebenezer Esenogho |
| author_sort | George Obaido |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T19:17:11Z |
| format | Article |
| id | doaj.art-7b9abae8064241768e6af3ed7aaf0a91 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T19:17:11Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-7b9abae8064241768e6af3ed7aaf0a912023-11-24T03:38:27ZengMDPI AGApplied Sciences2076-34172022-11-0112211112710.3390/app122111127An Interpretable Machine Learning Approach for Hepatitis B DiagnosisGeorge Obaido0Blessing Ogbuokiri1Theo G. Swart2Nimibofa Ayawei3Sydney Mambwe Kasongo4Kehinde Aruleba5Ibomoiye Domor Mienye6Idowu Aruleba7Williams Chukwu8Fadekemi Osaye9Oluwaseun F. Egbelowo10Simelane Simphiwe11Ebenezer Esenogho12Center for Human-Compatible Artificial Intelligence (CHAI), Berkeley Institute for Data Science (BIDS), University of California, Berkeley, CA 94720, USADepartment of Mathematics and Statistics, York University, Toronto, ON M3J 1P3, CanadaCenter for Telecommunications, Department of Electrical and Electronic Engineering Science, University of Johannesburg, Johannesburg 2006, South AfricaDepartment of Chemistry, Bayelsa Medical University, Yenagoa PMB 178, NigeriaDepartment of Industrial Engineering, Faculty of Engineering, Stellenbosch University, Stellenbosch 7600, South AfricaSchool of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UKDepartment of Electrical and Electronic Engineering Science, University of Johannesburg, Johannesburg 2006, South AfricaDepartment of Electrical and Electronic Engineering Science, University of Johannesburg, Johannesburg 2006, South AfricaDepartment of Mathematics, Wake Forest University, Winston-Salem, NC 27109, USADepartment of Mathematics and Computer Science, Alabama State University, Montgomery, AL 36104, USADepartment of Integrative Biology, The University of Texas at Austin, Austin, TX 78712, USADepartment of Mathematics and Applied Mathematics, University of Johannesburg, Doornfontein 2028, South AfricaCenter for Telecommunications, Department of Electrical and Electronic Engineering Science, University of Johannesburg, Johannesburg 2006, South AfricaHepatitis 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.https://www.mdpi.com/2076-3417/12/21/11127disease predictionhepatitis Binterpretabilitymachine learning |
| spellingShingle | George Obaido Blessing Ogbuokiri Theo G. Swart Nimibofa Ayawei Sydney Mambwe Kasongo Kehinde Aruleba Ibomoiye Domor Mienye Idowu Aruleba Williams Chukwu Fadekemi Osaye Oluwaseun F. Egbelowo Simelane Simphiwe Ebenezer Esenogho An Interpretable Machine Learning Approach for Hepatitis B Diagnosis Applied Sciences disease prediction hepatitis B interpretability machine learning |
| title | An Interpretable Machine Learning Approach for Hepatitis B Diagnosis |
| title_full | An Interpretable Machine Learning Approach for Hepatitis B Diagnosis |
| title_fullStr | An Interpretable Machine Learning Approach for Hepatitis B Diagnosis |
| title_full_unstemmed | An Interpretable Machine Learning Approach for Hepatitis B Diagnosis |
| title_short | An Interpretable Machine Learning Approach for Hepatitis B Diagnosis |
| title_sort | interpretable machine learning approach for hepatitis b diagnosis |
| topic | disease prediction hepatitis B interpretability machine learning |
| url | https://www.mdpi.com/2076-3417/12/21/11127 |
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