Early diabetes risk prediction using Ant Colony Optimization algorithm / Nur Aisyatul Husna Ahmad Yusri and Rizauddin Saian
Diabetes is a deadly disease that causes serious health complications to its sufferers. It costs the sufferers' health as well as their money. It is crucial to detect diabetes risk early to prevent the disease from worsening and becoming hard to treat. Therefore, this study has developed a clas...
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| Format: | Book Section |
| Language: | en |
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College of Computing, Informatics and Media, UiTM Perlis
2023
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| Online Access: | https://ir.uitm.edu.my/id/eprint/100257/1/100257.pdf https://ir.uitm.edu.my/id/eprint/100257/ |
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| _version_ | 1833321706238771200 |
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| author | Ahmad Yusri, Nur Aisyatul Husna Saian, Rizauddin |
| author_facet | Ahmad Yusri, Nur Aisyatul Husna Saian, Rizauddin |
| author_sort | Ahmad Yusri, Nur Aisyatul Husna |
| building | Tun Abdul Razak Library |
| collection | Institutional Repository |
| content_provider | Universiti Teknologi Mara |
| content_source | UiTM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Diabetes is a deadly disease that causes serious health complications to its sufferers. It costs the sufferers' health as well as their money. It is crucial to detect diabetes risk early to prevent the disease from worsening and becoming hard to treat. Therefore, this study has developed a classification model for predicting early diabetes risk using an Ant Colony Optimization (ACO) algorithm. The ACO-based classification algorithm, Ant-Miner is used to train the diabetes dataset of 520 new diabetes or potential diabetes patients from Sylhet Diabetes Hospital in Sylhet, Bangladesh. The average predictive accuracy from Ant-Miner is compared to the average predictive accuracy from J48. It is found that the average predictive accuracy of the model produced by Ant-Miner is at par with J48. The average predictive accuracy of the model produced by Ant-Miner is 95.51%, while J48 is 95.38%. |
| format | Book Section |
| id | my.uitm.ir-100257 |
| institution | Universiti Teknologi Mara |
| language | en |
| publishDate | 2023 |
| publisher | College of Computing, Informatics and Media, UiTM Perlis |
| record_format | eprints |
| spelling | my.uitm.ir-1002572024-09-26T16:37:37Z https://ir.uitm.edu.my/id/eprint/100257/ Early diabetes risk prediction using Ant Colony Optimization algorithm / Nur Aisyatul Husna Ahmad Yusri and Rizauddin Saian Ahmad Yusri, Nur Aisyatul Husna Saian, Rizauddin Algorithms Diabetes is a deadly disease that causes serious health complications to its sufferers. It costs the sufferers' health as well as their money. It is crucial to detect diabetes risk early to prevent the disease from worsening and becoming hard to treat. Therefore, this study has developed a classification model for predicting early diabetes risk using an Ant Colony Optimization (ACO) algorithm. The ACO-based classification algorithm, Ant-Miner is used to train the diabetes dataset of 520 new diabetes or potential diabetes patients from Sylhet Diabetes Hospital in Sylhet, Bangladesh. The average predictive accuracy from Ant-Miner is compared to the average predictive accuracy from J48. It is found that the average predictive accuracy of the model produced by Ant-Miner is at par with J48. The average predictive accuracy of the model produced by Ant-Miner is 95.51%, while J48 is 95.38%. College of Computing, Informatics and Media, UiTM Perlis 2023 Book Section PeerReviewed text en https://ir.uitm.edu.my/id/eprint/100257/1/100257.pdf Early diabetes risk prediction using Ant Colony Optimization algorithm / Nur Aisyatul Husna Ahmad Yusri and Rizauddin Saian. (2023) In: Research Exhibition in Mathematics and Computer Sciences (REMACS 5.0). College of Computing, Informatics and Media, UiTM Perlis, pp. 159-160. ISBN 978-629-97934-0-3 |
| spellingShingle | Algorithms Ahmad Yusri, Nur Aisyatul Husna Saian, Rizauddin Early diabetes risk prediction using Ant Colony Optimization algorithm / Nur Aisyatul Husna Ahmad Yusri and Rizauddin Saian |
| title | Early diabetes risk prediction using Ant Colony Optimization algorithm / Nur Aisyatul Husna Ahmad Yusri and Rizauddin Saian |
| title_full | Early diabetes risk prediction using Ant Colony Optimization algorithm / Nur Aisyatul Husna Ahmad Yusri and Rizauddin Saian |
| title_fullStr | Early diabetes risk prediction using Ant Colony Optimization algorithm / Nur Aisyatul Husna Ahmad Yusri and Rizauddin Saian |
| title_full_unstemmed | Early diabetes risk prediction using Ant Colony Optimization algorithm / Nur Aisyatul Husna Ahmad Yusri and Rizauddin Saian |
| title_short | Early diabetes risk prediction using Ant Colony Optimization algorithm / Nur Aisyatul Husna Ahmad Yusri and Rizauddin Saian |
| title_sort | early diabetes risk prediction using ant colony optimization algorithm / nur aisyatul husna ahmad yusri and rizauddin saian |
| topic | Algorithms |
| url | https://ir.uitm.edu.my/id/eprint/100257/1/100257.pdf https://ir.uitm.edu.my/id/eprint/100257/ |
| url_provider | http://ir.uitm.edu.my/ |