Artificial Intelligence and Machine Learning Based Prediction of Viral Load and CD4 Status of People Living with HIV (PLWH) on Anti-Retroviral Treatment in Gedeo Zone Public Hospitals
Binyam Tariku Seboka, Delelegn Emwodew Yehualashet, Getanew Aschalew Tesfa School of Public Health, Dilla University, Dilla, EthiopiaCorrespondence: Binyam Tariku Seboka, School of public health, Dilla University, P.O Box: 419, Dilla University, Dilla, Ethiopia, Tel +251 920612180, Fax +251 46-331-2...
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| Format: | Article |
| Language: | English |
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Dove Medical Press
2023-02-01
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| Series: | International Journal of General Medicine |
| Subjects: | |
| Online Access: | https://www.dovepress.com/artificial-intelligence-and-machine-learning-based-prediction-of-viral-peer-reviewed-fulltext-article-IJGM |
| _version_ | 1811169564233302016 |
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| author | Seboka BT Yehualashet DE Tesfa GA |
| author_facet | Seboka BT Yehualashet DE Tesfa GA |
| author_sort | Seboka BT |
| collection | DOAJ |
| description | Binyam Tariku Seboka, Delelegn Emwodew Yehualashet, Getanew Aschalew Tesfa School of Public Health, Dilla University, Dilla, EthiopiaCorrespondence: Binyam Tariku Seboka, School of public health, Dilla University, P.O Box: 419, Dilla University, Dilla, Ethiopia, Tel +251 920612180, Fax +251 46-331-2568, Email bini555tar@gmail.comBackground: Despite the success made in scaling up HIV treatment activities, there remains a tremendous unmet demand for the monitoring of the disease progression and treatment success, which threatens HIV/AIDS treatment and control. This research presented the assessments of viral load and CD4 classification of adults enrolled in ART care using machine learning algorithms.Methods: We trained, validated, and tested eight machine learning (ML) classifier algorithms with historical data, including demographics, clinical, and laboratory data. Data were extracted from the ART registry database of Yirgacheffe Primary Hospital and Dilla University Referral Hospital. ML classifiers were trained to predict virological failure (viral load > 1000 copies/mL) and poor CD4 (CD4 cell count < 200 cells/mL). The model predictive performances were evaluated using accuracy, sensitivity, specificity, precision, f1-score, F-beta scores, and AUC.Results: The mean age of the sample participants was 41.6 years (SD = 10.9). The experimental results showed that XGB classifier ranked as the best algorithm for viral load prediction in terms of sensitivity (97%), f1-score (96%), AUC (0.99), accuracy (96%), followed by RF. The GB classifier exhibited a better predictive capability in predicting participants with a CD4 cell count < 200 cells/mL.Conclusion: In this study, the XGB and RF models had the highest accuracy and outperformed on various evaluation metrics among the models examined for viral load classification. In the prediction of participants CD4, GB model had the highest accuracy.Keywords: artificial intelligence, AI, machine learning, ML, anti-retroviral treatment, ART, viral load, CD4 count, HIV |
| first_indexed | 2024-04-10T16:44:39Z |
| format | Article |
| id | doaj.art-a6063e53f7634c8a8d4e4712788131b9 |
| institution | Directory Open Access Journal |
| issn | 1178-7074 |
| language | English |
| last_indexed | 2024-04-10T16:44:39Z |
| publishDate | 2023-02-01 |
| publisher | Dove Medical Press |
| record_format | Article |
| series | International Journal of General Medicine |
| spelling | doaj.art-a6063e53f7634c8a8d4e4712788131b92023-02-08T01:41:53ZengDove Medical PressInternational Journal of General Medicine1178-70742023-02-01Volume 1643545181434Artificial Intelligence and Machine Learning Based Prediction of Viral Load and CD4 Status of People Living with HIV (PLWH) on Anti-Retroviral Treatment in Gedeo Zone Public HospitalsSeboka BTYehualashet DETesfa GABinyam Tariku Seboka, Delelegn Emwodew Yehualashet, Getanew Aschalew Tesfa School of Public Health, Dilla University, Dilla, EthiopiaCorrespondence: Binyam Tariku Seboka, School of public health, Dilla University, P.O Box: 419, Dilla University, Dilla, Ethiopia, Tel +251 920612180, Fax +251 46-331-2568, Email bini555tar@gmail.comBackground: Despite the success made in scaling up HIV treatment activities, there remains a tremendous unmet demand for the monitoring of the disease progression and treatment success, which threatens HIV/AIDS treatment and control. This research presented the assessments of viral load and CD4 classification of adults enrolled in ART care using machine learning algorithms.Methods: We trained, validated, and tested eight machine learning (ML) classifier algorithms with historical data, including demographics, clinical, and laboratory data. Data were extracted from the ART registry database of Yirgacheffe Primary Hospital and Dilla University Referral Hospital. ML classifiers were trained to predict virological failure (viral load > 1000 copies/mL) and poor CD4 (CD4 cell count < 200 cells/mL). The model predictive performances were evaluated using accuracy, sensitivity, specificity, precision, f1-score, F-beta scores, and AUC.Results: The mean age of the sample participants was 41.6 years (SD = 10.9). The experimental results showed that XGB classifier ranked as the best algorithm for viral load prediction in terms of sensitivity (97%), f1-score (96%), AUC (0.99), accuracy (96%), followed by RF. The GB classifier exhibited a better predictive capability in predicting participants with a CD4 cell count < 200 cells/mL.Conclusion: In this study, the XGB and RF models had the highest accuracy and outperformed on various evaluation metrics among the models examined for viral load classification. In the prediction of participants CD4, GB model had the highest accuracy.Keywords: artificial intelligence, AI, machine learning, ML, anti-retroviral treatment, ART, viral load, CD4 count, HIVhttps://www.dovepress.com/artificial-intelligence-and-machine-learning-based-prediction-of-viral-peer-reviewed-fulltext-article-IJGMartificial intelligenceaimachine learningmlanti-retroviral treatmentartviral loadcd4 counthiv. |
| spellingShingle | Seboka BT Yehualashet DE Tesfa GA Artificial Intelligence and Machine Learning Based Prediction of Viral Load and CD4 Status of People Living with HIV (PLWH) on Anti-Retroviral Treatment in Gedeo Zone Public Hospitals International Journal of General Medicine artificial intelligence ai machine learning ml anti-retroviral treatment art viral load cd4 count hiv. |
| title | Artificial Intelligence and Machine Learning Based Prediction of Viral Load and CD4 Status of People Living with HIV (PLWH) on Anti-Retroviral Treatment in Gedeo Zone Public Hospitals |
| title_full | Artificial Intelligence and Machine Learning Based Prediction of Viral Load and CD4 Status of People Living with HIV (PLWH) on Anti-Retroviral Treatment in Gedeo Zone Public Hospitals |
| title_fullStr | Artificial Intelligence and Machine Learning Based Prediction of Viral Load and CD4 Status of People Living with HIV (PLWH) on Anti-Retroviral Treatment in Gedeo Zone Public Hospitals |
| title_full_unstemmed | Artificial Intelligence and Machine Learning Based Prediction of Viral Load and CD4 Status of People Living with HIV (PLWH) on Anti-Retroviral Treatment in Gedeo Zone Public Hospitals |
| title_short | Artificial Intelligence and Machine Learning Based Prediction of Viral Load and CD4 Status of People Living with HIV (PLWH) on Anti-Retroviral Treatment in Gedeo Zone Public Hospitals |
| title_sort | artificial intelligence and machine learning based prediction of viral load and cd4 status of people living with hiv plwh on anti retroviral treatment in gedeo zone public hospitals |
| topic | artificial intelligence ai machine learning ml anti-retroviral treatment art viral load cd4 count hiv. |
| url | https://www.dovepress.com/artificial-intelligence-and-machine-learning-based-prediction-of-viral-peer-reviewed-fulltext-article-IJGM |
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