Computer-aided system for extending the performance of diabetes analysis and prediction
Every year, diabetes causes health difficulties for hundreds of millions of individuals throughout the world. Patients’ medical records may be utilized to quantify symptoms, physical characteristics, and clinical laboratory test data, which may then be utilized to undertake biostatistics analysis to...
| Main Authors: | , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English English |
| Published: |
IEEE
2021
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/34577/ http://umpir.ump.edu.my/id/eprint/34577/1/Computer-aided%20system%20for%20extending%20the%20performance%20of%20diabetes%20analysis%20.pdf http://umpir.ump.edu.my/id/eprint/34577/2/Computer-aided%20system%20for%20extending%20the%20performance%20of%20diabetes%20analysis_FULL.pdf |
| _version_ | 1848824543224791040 |
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| author | Murad, Saydul Akbar Zafril Rizal, M Azmi Zaid Hafiz, Hakami Prottasha, Nusrat Jahan Kowsher, Md |
| author_facet | Murad, Saydul Akbar Zafril Rizal, M Azmi Zaid Hafiz, Hakami Prottasha, Nusrat Jahan Kowsher, Md |
| author_sort | Murad, Saydul Akbar |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Every year, diabetes causes health difficulties for hundreds of millions of individuals throughout the world. Patients’ medical records may be utilized to quantify symptoms, physical characteristics, and clinical laboratory test data, which may then be utilized to undertake biostatistics analysis to uncover patterns or characteristics that are now undetected. In this work, we have used six machine learning algorithms to give the prediction of diabetes patients and the reason for diabetes are illustrated in percentage using pie charts. The machine learning algorithms used to predict the risks of Type 2 diabetes. User can self-assess their diabetes risk once the model has been trained. Based on the experimental results in AdaBoost Classifier's, the accuracy achieved is almost 98 percent. |
| first_indexed | 2025-11-15T03:14:42Z |
| format | Conference or Workshop Item |
| id | ump-34577 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-15T03:14:42Z |
| publishDate | 2021 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-345772022-07-04T01:58:55Z http://umpir.ump.edu.my/id/eprint/34577/ Computer-aided system for extending the performance of diabetes analysis and prediction Murad, Saydul Akbar Zafril Rizal, M Azmi Zaid Hafiz, Hakami Prottasha, Nusrat Jahan Kowsher, Md QA76 Computer software Every year, diabetes causes health difficulties for hundreds of millions of individuals throughout the world. Patients’ medical records may be utilized to quantify symptoms, physical characteristics, and clinical laboratory test data, which may then be utilized to undertake biostatistics analysis to uncover patterns or characteristics that are now undetected. In this work, we have used six machine learning algorithms to give the prediction of diabetes patients and the reason for diabetes are illustrated in percentage using pie charts. The machine learning algorithms used to predict the risks of Type 2 diabetes. User can self-assess their diabetes risk once the model has been trained. Based on the experimental results in AdaBoost Classifier's, the accuracy achieved is almost 98 percent. IEEE 2021-08 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/34577/1/Computer-aided%20system%20for%20extending%20the%20performance%20of%20diabetes%20analysis%20.pdf pdf en http://umpir.ump.edu.my/id/eprint/34577/2/Computer-aided%20system%20for%20extending%20the%20performance%20of%20diabetes%20analysis_FULL.pdf Murad, Saydul Akbar and Zafril Rizal, M Azmi and Zaid Hafiz, Hakami and Prottasha, Nusrat Jahan and Kowsher, Md (2021) Computer-aided system for extending the performance of diabetes analysis and prediction. In: 7th International Conference on Software Engineering and Computer Systems and 4th International Conference on Computational Science and Information Management, ICSECS-ICOCSIM 2021 , 24-26 Aug. 2021 , Pekan, Malaysia. 465 -470.. ISBN 978-166541407-4 (Published) https://doi.org/10.1109/ICSECS52883.2021.00091 |
| spellingShingle | QA76 Computer software Murad, Saydul Akbar Zafril Rizal, M Azmi Zaid Hafiz, Hakami Prottasha, Nusrat Jahan Kowsher, Md Computer-aided system for extending the performance of diabetes analysis and prediction |
| title | Computer-aided system for extending the performance of diabetes analysis and prediction |
| title_full | Computer-aided system for extending the performance of diabetes analysis and prediction |
| title_fullStr | Computer-aided system for extending the performance of diabetes analysis and prediction |
| title_full_unstemmed | Computer-aided system for extending the performance of diabetes analysis and prediction |
| title_short | Computer-aided system for extending the performance of diabetes analysis and prediction |
| title_sort | computer-aided system for extending the performance of diabetes analysis and prediction |
| topic | QA76 Computer software |
| url | http://umpir.ump.edu.my/id/eprint/34577/ http://umpir.ump.edu.my/id/eprint/34577/ http://umpir.ump.edu.my/id/eprint/34577/1/Computer-aided%20system%20for%20extending%20the%20performance%20of%20diabetes%20analysis%20.pdf http://umpir.ump.edu.my/id/eprint/34577/2/Computer-aided%20system%20for%20extending%20the%20performance%20of%20diabetes%20analysis_FULL.pdf |