The Long-Term Prediction of Type II Diabetes Mellitus: A Review Study
Type II Diabetes Mellitus is one of the silent killer diseases worldwide. According to the World Health Organization, 346 million people are suffering from diabetes worldwide. In this paper, we reviewed some models and systems used for diabetes prediction, comparing and exploring various related res...
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ump-35422018-10-04T08:21:07Z http://umpir.ump.edu.my/id/eprint/3542/ The Long-Term Prediction of Type II Diabetes Mellitus: A Review Study Amatul, Zehra Tuty Asmawaty, Abdul Kadir Sabira, Khatun QA Mathematics RC Internal medicine Type II Diabetes Mellitus is one of the silent killer diseases worldwide. According to the World Health Organization, 346 million people are suffering from diabetes worldwide. In this paper, we reviewed some models and systems used for diabetes prediction, comparing and exploring various related research works. We found some models and systems to predict and diagnose type II diabetes mainly for a short term. This led to the related open issues of the development of a model for the prediction of type II diabetes in the long run. We analysed the need of a relation between the major factors that lead to the development of diabetes. We aim to study the pattern behaviour in existing data. Furthermore, we have proposed to classify the data using k-means algorithm and the prediction through particle filter method. The model would be able to do long term prediction for potential persons. This would be beneficial to overcome the sharp rise of diabetes globally. 2012 Conference or Workshop Item PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/3542/1/28-ICoCSIM.pdf Amatul, Zehra and Tuty Asmawaty, Abdul Kadir and Sabira, Khatun (2012) The Long-Term Prediction of Type II Diabetes Mellitus: A Review Study. In: International Conference on Computational Science and Information Management (ICoCSIM), 3-5 December 2012 , Toba Lake, North Sumatera, Indonesia. pp. 140-146.. |
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QA Mathematics RC Internal medicine Amatul, Zehra Tuty Asmawaty, Abdul Kadir Sabira, Khatun The Long-Term Prediction of Type II Diabetes Mellitus: A Review Study |
description |
Type II Diabetes Mellitus is one of the silent killer diseases worldwide. According to the World Health Organization, 346 million people are suffering from diabetes worldwide. In this paper, we reviewed some models and systems used for diabetes prediction, comparing and exploring various related research works. We found some models and systems to predict and diagnose type II diabetes mainly for a short term. This led to the related open issues of the development of a model for the prediction of type II diabetes in the long run. We analysed the need of a relation between the major factors that lead to the development of diabetes. We aim to study the pattern behaviour in existing data. Furthermore, we have proposed to classify the data using k-means algorithm and the prediction through particle filter method. The model would be able to do long term prediction for potential persons. This would be beneficial to overcome the sharp rise of diabetes globally. |
format |
Conference or Workshop Item |
author |
Amatul, Zehra Tuty Asmawaty, Abdul Kadir Sabira, Khatun |
author_facet |
Amatul, Zehra Tuty Asmawaty, Abdul Kadir Sabira, Khatun |
author_sort |
Amatul, Zehra |
title |
The Long-Term Prediction of Type II Diabetes Mellitus: A Review Study |
title_short |
The Long-Term Prediction of Type II Diabetes Mellitus: A Review Study |
title_full |
The Long-Term Prediction of Type II Diabetes Mellitus: A Review Study |
title_fullStr |
The Long-Term Prediction of Type II Diabetes Mellitus: A Review Study |
title_full_unstemmed |
The Long-Term Prediction of Type II Diabetes Mellitus: A Review Study |
title_sort |
long-term prediction of type ii diabetes mellitus: a review study |
publishDate |
2012 |
url |
http://umpir.ump.edu.my/id/eprint/3542/ http://umpir.ump.edu.my/id/eprint/3542/1/28-ICoCSIM.pdf |
first_indexed |
2023-09-18T21:57:54Z |
last_indexed |
2023-09-18T21:57:54Z |
_version_ |
1777414202438189056 |