Enhanced detection of diabetes mellitus using novel ensemble feature engineering approach and machine learning model
Article
Subjects > Biomedicine
Subjects > Engineering
Europe University of Atlantic > Research > Articles and books
Ibero-american International University > Research > Scientific Production
Ibero-american International University > Research > Scientific Production
Universidad Internacional do Cuanza > Research > Scientific Production
University of La Romana > Research > Scientific Production
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Inglés
Diabetes is a persistent health condition led by insufficient use or inappropriate use of insulin in the body. If left undetected, it can lead to further complications involving organ damage such as heart, lungs, and eyes. Timely detection of diabetes helps obtain the right medication, diet, and exercise plan to lead a healthy life. ML approach has been utilized to obtain rapid and reliable diabetes detection, however, existing approaches suffer from the use of limited datasets, lack of generalizability, and lower accuracy. This study proposes a novel feature extraction approach to overcome these limitations by using an ensemble of convolutional neural network (CNN) and long short-term memory (LSTM) models. Multiple datasets are combined to make a larger dataset for experiments and multiple features are utilized for investigating the efficacy of the proposed approach. Features from the extra tree classifier, CNN, and LSTM are also considered for comparison. Experimental results reveal the superb performance of CNN-LSTM-based features with random forest model obtaining a 0.99 accuracy score. This performance is further validated by comparison with existing approaches and k-fold cross-validation which shows the proposed approach provides robust results.
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Rustam, Furqan and Al-Shamayleh, Ahmad Sami and Shafique, Rahman and Aparicio Obregón, Silvia and Calderón Iglesias, Rubén and Gonzalez, J. Pablo Miramontes and Ashraf, Imran
mail
UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, silvia.aparicio@uneatlantico.es, ruben.calderon@uneatlantico.es, UNSPECIFIED, UNSPECIFIED
(2024)
Enhanced detection of diabetes mellitus using novel ensemble feature engineering approach and machine learning model.
Scientific Reports, 14 (1).
ISSN 2045-2322
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Text
s41598-024-74357-w.pdf Available under License Creative Commons Attribution Non-commercial No Derivatives. Download (3MB) | Preview |
Abstract
Diabetes is a persistent health condition led by insufficient use or inappropriate use of insulin in the body. If left undetected, it can lead to further complications involving organ damage such as heart, lungs, and eyes. Timely detection of diabetes helps obtain the right medication, diet, and exercise plan to lead a healthy life. ML approach has been utilized to obtain rapid and reliable diabetes detection, however, existing approaches suffer from the use of limited datasets, lack of generalizability, and lower accuracy. This study proposes a novel feature extraction approach to overcome these limitations by using an ensemble of convolutional neural network (CNN) and long short-term memory (LSTM) models. Multiple datasets are combined to make a larger dataset for experiments and multiple features are utilized for investigating the efficacy of the proposed approach. Features from the extra tree classifier, CNN, and LSTM are also considered for comparison. Experimental results reveal the superb performance of CNN-LSTM-based features with random forest model obtaining a 0.99 accuracy score. This performance is further validated by comparison with existing approaches and k-fold cross-validation which shows the proposed approach provides robust results.
Item Type: | Article |
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Uncontrolled Keywords: | ZeroShot learning; Transfer learning; Spider mites detection; Plants health; Zeroshot CNN |
Subjects: | Subjects > Biomedicine Subjects > Engineering |
Divisions: | Europe University of Atlantic > Research > Articles and books Ibero-american International University > Research > Scientific Production Ibero-american International University > Research > Scientific Production Universidad Internacional do Cuanza > Research > Scientific Production University of La Romana > Research > Scientific Production |
Date Deposited: | 29 Oct 2024 23:30 |
Last Modified: | 29 Oct 2024 23:30 |
URI: | https://repositorio.uneatlantico.es/id/eprint/14915 |
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