TY  - JOUR
UR  - http://doi.org/10.1038/s41598-024-74357-w
ID  - uneatlantico14915
KW  - ZeroShot learning; Transfer learning; Spider mites detection; Plants health; Zeroshot CNN
IS  - 1
VL  - 14
Y1  - 2024/10//
TI  - Enhanced detection of diabetes mellitus using novel ensemble feature engineering approach and machine learning model
JF  - Scientific Reports
AV  - public
SN  - 2045-2322
N2  - 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.
A1  - Rustam, Furqan
A1  - Al-Shamayleh, Ahmad Sami
A1  - Shafique, Rahman
A1  - Aparicio Obregón, Silvia
A1  - Calderón Iglesias, Rubén
A1  - Gonzalez, J. Pablo Miramontes
A1  - Ashraf, Imran
ER  -