Advanced Techniques for Predicting the Future Progression of Type 2 Diabetes
<p dir="ltr">Diabetes is a costly and burdensome metabolic disorder that occurs due to the elevation of glucose levels in the bloodstream. If it goes unchecked for an extended period, it can lead to the damage of different body organs and develop life-threatening health complications...
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2020
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| _version_ | 1864513505609121792 |
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| author | Md. Shafiqul Islam (8988548) |
| author2 | Marwa K. Qaraqe (8115020) Samir Brahim Belhaouari (9427347) Muhammad A. Abdul-Ghani (10285402) |
| author2_role | author author author |
| author_facet | Md. Shafiqul Islam (8988548) Marwa K. Qaraqe (8115020) Samir Brahim Belhaouari (9427347) Muhammad A. Abdul-Ghani (10285402) |
| author_role | author |
| dc.creator.none.fl_str_mv | Md. Shafiqul Islam (8988548) Marwa K. Qaraqe (8115020) Samir Brahim Belhaouari (9427347) Muhammad A. Abdul-Ghani (10285402) |
| dc.date.none.fl_str_mv | 2020-06-29T12:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2020.3005540 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Advanced_Techniques_for_Predicting_the_Future_Progression_of_Type_2_Diabetes/27021340 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biomedical and clinical sciences Medical biochemistry and metabolomics Health sciences Health services and systems Information and computing sciences Machine learning Feature extraction fractional derivative wavelet transform machine learning diabetes prediction Diabetes Glucose Machine learning Insulin Plasmas Data models |
| dc.title.none.fl_str_mv | Advanced Techniques for Predicting the Future Progression of Type 2 Diabetes |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Diabetes is a costly and burdensome metabolic disorder that occurs due to the elevation of glucose levels in the bloodstream. If it goes unchecked for an extended period, it can lead to the damage of different body organs and develop life-threatening health complications. Studies show that the progression of diabetes can be stopped or delayed, provided a person follows a healthy lifestyle and takes proper medication. Prevention of diabetes or the delayed onset of diabetes is crucial, and it can be achieved if there exists a screening process that identifies individuals who are at risk of developing diabetes in the future. Although machine learning techniques have been applied for disease diagnosis, there is little work done on long term prediction of disease, type 2 diabetes in particular. Moreover, finding discriminative features or risk-factors responsible for the future development of diabetes plays a significant role. In this study, we propose two novel feature extraction approaches for finding the best risk-factors, followed by applying a machine learning pipeline for the long term prediction of type 2 diabetes. The proposed methods have been evaluated using data from a longitudinal clinical study, known as the San Antonio Heart Study. Our proposed model managed to achieve 95.94% accuracy in predicting whether a person will develop type 2 diabetes within the next 7-8 years or not.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2020.3005540" target="_blank">https://dx.doi.org/10.1109/access.2020.3005540</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_d50cc060eb13bce9385d1546376f6a0a |
| identifier_str_mv | 10.1109/access.2020.3005540 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/27021340 |
| publishDate | 2020 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Advanced Techniques for Predicting the Future Progression of Type 2 DiabetesMd. Shafiqul Islam (8988548)Marwa K. Qaraqe (8115020)Samir Brahim Belhaouari (9427347)Muhammad A. Abdul-Ghani (10285402)Biomedical and clinical sciencesMedical biochemistry and metabolomicsHealth sciencesHealth services and systemsInformation and computing sciencesMachine learningFeature extractionfractional derivativewavelet transformmachine learningdiabetes predictionDiabetesGlucoseMachine learningInsulinPlasmasData models<p dir="ltr">Diabetes is a costly and burdensome metabolic disorder that occurs due to the elevation of glucose levels in the bloodstream. If it goes unchecked for an extended period, it can lead to the damage of different body organs and develop life-threatening health complications. Studies show that the progression of diabetes can be stopped or delayed, provided a person follows a healthy lifestyle and takes proper medication. Prevention of diabetes or the delayed onset of diabetes is crucial, and it can be achieved if there exists a screening process that identifies individuals who are at risk of developing diabetes in the future. Although machine learning techniques have been applied for disease diagnosis, there is little work done on long term prediction of disease, type 2 diabetes in particular. Moreover, finding discriminative features or risk-factors responsible for the future development of diabetes plays a significant role. In this study, we propose two novel feature extraction approaches for finding the best risk-factors, followed by applying a machine learning pipeline for the long term prediction of type 2 diabetes. The proposed methods have been evaluated using data from a longitudinal clinical study, known as the San Antonio Heart Study. Our proposed model managed to achieve 95.94% accuracy in predicting whether a person will develop type 2 diabetes within the next 7-8 years or not.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2020.3005540" target="_blank">https://dx.doi.org/10.1109/access.2020.3005540</a></p>2020-06-29T12:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2020.3005540https://figshare.com/articles/journal_contribution/Advanced_Techniques_for_Predicting_the_Future_Progression_of_Type_2_Diabetes/27021340CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/270213402020-06-29T12:00:00Z |
| spellingShingle | Advanced Techniques for Predicting the Future Progression of Type 2 Diabetes Md. Shafiqul Islam (8988548) Biomedical and clinical sciences Medical biochemistry and metabolomics Health sciences Health services and systems Information and computing sciences Machine learning Feature extraction fractional derivative wavelet transform machine learning diabetes prediction Diabetes Glucose Machine learning Insulin Plasmas Data models |
| status_str | publishedVersion |
| title | Advanced Techniques for Predicting the Future Progression of Type 2 Diabetes |
| title_full | Advanced Techniques for Predicting the Future Progression of Type 2 Diabetes |
| title_fullStr | Advanced Techniques for Predicting the Future Progression of Type 2 Diabetes |
| title_full_unstemmed | Advanced Techniques for Predicting the Future Progression of Type 2 Diabetes |
| title_short | Advanced Techniques for Predicting the Future Progression of Type 2 Diabetes |
| title_sort | Advanced Techniques for Predicting the Future Progression of Type 2 Diabetes |
| topic | Biomedical and clinical sciences Medical biochemistry and metabolomics Health sciences Health services and systems Information and computing sciences Machine learning Feature extraction fractional derivative wavelet transform machine learning diabetes prediction Diabetes Glucose Machine learning Insulin Plasmas Data models |