Dengue Outbreak Detection Model Using Artificial Immune System: A Malaysian Case Study
Dengue is a virus that is spreading quickly and poses a severe threat in Malaysia. It is essential to have an accurate early detection system that can trigger prompt response, reducing deaths and morbidity. Nevertheless, uncertainties in the dengue outbreak dataset reduce the robustness of existing...
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| Format: | Article |
| Language: | English |
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Universiti Utara Malaysia Press
2023
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| Online Access: | https://repo.uum.edu.my/id/eprint/29667/1/JICT%2022%2003%202023%20399-419.pdf https://doi.org/10.32890/jict2023.22.3.4 |
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| author | Mohamad Mohsin, Mohamad Farhan Abu Bakar, Azuraliza Hamdan, Abdul Razak Sahani, Mazrura Mohd Ali, Zainudin |
| author_facet | Mohamad Mohsin, Mohamad Farhan Abu Bakar, Azuraliza Hamdan, Abdul Razak Sahani, Mazrura Mohd Ali, Zainudin |
| author_sort | Mohamad Mohsin, Mohamad Farhan |
| collection | UUM |
| description | Dengue is a virus that is spreading quickly and poses a severe threat in Malaysia. It is essential to have an accurate early detection system that can trigger prompt response, reducing deaths and morbidity. Nevertheless, uncertainties in the dengue outbreak dataset reduce the robustness of existing detection models, which require a training phase and thus fail to detect previously unseen outbreak patterns. Consequently, the model fails to detect newly discovered outbreak patterns. This outcome leads to inaccurate decision-making and delays in implementing prevention plans. Anomaly detection and other detection-based problems have already been widely implemented with some success using danger theory (DT), a variation of the artificial immune system and a nature-inspired computer technique. Therefore, this study employed DT to develop a novel outbreak detection model. A Malaysian dengue profile dataset was used for the experiment. The results revealed that the proposed DT model performed better than existing methods and significantly improved dengue outbreak detection. The findings demonstrated that the inclusion of a DT detection mechanism enhanced the dengue outbreak detection model’s accuracy. Even without a training phase, the proposed model consistently demonstrated high sensitivity, high specificity, high accuracy, and lower false alarm rate for distinguishing between outbreak and non-outbreak instances. |
| first_indexed | 2024-07-04T06:42:21Z |
| format | Article |
| id | uum-29667 |
| institution | Universiti Utara Malaysia |
| language | English |
| last_indexed | 2024-07-04T06:42:21Z |
| publishDate | 2023 |
| publisher | Universiti Utara Malaysia Press |
| record_format | eprints |
| spelling | uum-296672023-07-31T09:58:11Z https://repo.uum.edu.my/id/eprint/29667/ Dengue Outbreak Detection Model Using Artificial Immune System: A Malaysian Case Study Mohamad Mohsin, Mohamad Farhan Abu Bakar, Azuraliza Hamdan, Abdul Razak Sahani, Mazrura Mohd Ali, Zainudin T Technology (General) Dengue is a virus that is spreading quickly and poses a severe threat in Malaysia. It is essential to have an accurate early detection system that can trigger prompt response, reducing deaths and morbidity. Nevertheless, uncertainties in the dengue outbreak dataset reduce the robustness of existing detection models, which require a training phase and thus fail to detect previously unseen outbreak patterns. Consequently, the model fails to detect newly discovered outbreak patterns. This outcome leads to inaccurate decision-making and delays in implementing prevention plans. Anomaly detection and other detection-based problems have already been widely implemented with some success using danger theory (DT), a variation of the artificial immune system and a nature-inspired computer technique. Therefore, this study employed DT to develop a novel outbreak detection model. A Malaysian dengue profile dataset was used for the experiment. The results revealed that the proposed DT model performed better than existing methods and significantly improved dengue outbreak detection. The findings demonstrated that the inclusion of a DT detection mechanism enhanced the dengue outbreak detection model’s accuracy. Even without a training phase, the proposed model consistently demonstrated high sensitivity, high specificity, high accuracy, and lower false alarm rate for distinguishing between outbreak and non-outbreak instances. Universiti Utara Malaysia Press 2023 Article PeerReviewed application/pdf en cc4_by https://repo.uum.edu.my/id/eprint/29667/1/JICT%2022%2003%202023%20399-419.pdf Mohamad Mohsin, Mohamad Farhan and Abu Bakar, Azuraliza and Hamdan, Abdul Razak and Sahani, Mazrura and Mohd Ali, Zainudin (2023) Dengue Outbreak Detection Model Using Artificial Immune System: A Malaysian Case Study. Journal of Information and Communication Technology, 22 (3). pp. 399-419. ISSN 2180-3862 https://e-journal.uum.edu.my/index.php/jict/article/view/17184 https://doi.org/10.32890/jict2023.22.3.4 https://doi.org/10.32890/jict2023.22.3.4 |
| spellingShingle | T Technology (General) Mohamad Mohsin, Mohamad Farhan Abu Bakar, Azuraliza Hamdan, Abdul Razak Sahani, Mazrura Mohd Ali, Zainudin Dengue Outbreak Detection Model Using Artificial Immune System: A Malaysian Case Study |
| title | Dengue Outbreak Detection Model Using Artificial Immune System: A Malaysian Case Study |
| title_full | Dengue Outbreak Detection Model Using Artificial Immune System: A Malaysian Case Study |
| title_fullStr | Dengue Outbreak Detection Model Using Artificial Immune System: A Malaysian Case Study |
| title_full_unstemmed | Dengue Outbreak Detection Model Using Artificial Immune System: A Malaysian Case Study |
| title_short | Dengue Outbreak Detection Model Using Artificial Immune System: A Malaysian Case Study |
| title_sort | dengue outbreak detection model using artificial immune system a malaysian case study |
| topic | T Technology (General) |
| url | https://repo.uum.edu.my/id/eprint/29667/1/JICT%2022%2003%202023%20399-419.pdf https://doi.org/10.32890/jict2023.22.3.4 |
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