Version 1
: Received: 11 January 2023 / Approved: 12 January 2023 / Online: 12 January 2023 (03:54:15 CET)
How to cite:
Olabanjo, O.; Wusu, A.; Mazzara, M. Deep Unsupervised Machine Learning for Early Diabetes Risk Prediction using Ensemble Feature Selection and Deep Belief Neural Networks. Preprints2023, 2023010208. https://doi.org/10.20944/preprints202301.0208.v1
Olabanjo, O.; Wusu, A.; Mazzara, M. Deep Unsupervised Machine Learning for Early Diabetes Risk Prediction using Ensemble Feature Selection and Deep Belief Neural Networks. Preprints 2023, 2023010208. https://doi.org/10.20944/preprints202301.0208.v1
Olabanjo, O.; Wusu, A.; Mazzara, M. Deep Unsupervised Machine Learning for Early Diabetes Risk Prediction using Ensemble Feature Selection and Deep Belief Neural Networks. Preprints2023, 2023010208. https://doi.org/10.20944/preprints202301.0208.v1
APA Style
Olabanjo, O., Wusu, A., & Mazzara, M. (2023). Deep Unsupervised Machine Learning for Early Diabetes Risk Prediction using Ensemble Feature Selection and Deep Belief Neural Networks. Preprints. https://doi.org/10.20944/preprints202301.0208.v1
Chicago/Turabian Style
Olabanjo, O., Ashiribo Wusu and Manuel Mazzara. 2023 "Deep Unsupervised Machine Learning for Early Diabetes Risk Prediction using Ensemble Feature Selection and Deep Belief Neural Networks" Preprints. https://doi.org/10.20944/preprints202301.0208.v1
Abstract
Diabetes mellitus is a popular life-threatening disease and patients may gradually have started suffering from other diabetes-causing diseases such as heart attacks, stroke, hypertension, blurry vision, blindness, foot ulcer, amputation, kidney damage and other organ failures before diagnosis. Early detection can help reduce the fatality of this disease. Deep learning models have proven very useful in disease detection and computer-aided diagnosis. In this work, we proposed a deep unsupervised machine learning model for early detection of diabetes using voting ensemble feature selection and deep belief neural networks (DBN). Dataset was obtained from an online repository containing responses of prediagnosed patients to direct questionnaires administered in Sylhet Diabetes Hospital in Sylhet, Bangladesh. The dataset was preprocessed and preprocessed. Features were reduced using the ensemble feature selector. The DBN model was pretrained and tuned to obtain optimal performance. The model was also compared with other models with no multiple hidden layers. The DBN performed at its relative best with F1-measure, precision and recall of 1.00, 0.92 and 1.00 respectively. We conclude that DBN is a useful tool for an unsupervised early prediction of Type II diabetes mellitus.
Keywords
Deep belief network; Diabetes; Prediction; Risk Factors; Deep Learning
Subject
Physical Sciences, Biophysics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
