Artificial Intelligence - Enabled Deep Learning Model for Diabetes Prediction Using Deep Belief Network with Bayesian Optimization

Diabetes is one of the major health issues that affect more than 10.5 percent of the adult population across the globe. This study applied deep learning techniques of deep belief network (DBN), long short-term memory (LSTM) and recurrent neural network (RNN) with Bayesian optimization on a diabetes...

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Bibliographic Details
Main Authors: Akinsola, Jide Ebenezer Taiwo, Ajagbe, Sunday Adeola, Olajubu, Emmanuel Ajayi, Lawal, Azeezat Oluwayemisi, Aderounmu, Ganiyu Adesola, Adigun, Matthew Olusegun
Format: Conference Proceeding
Language:English
Subjects:
Accuracy
bayesian optimization
deep belief network
Deep learning
Diabetes
Performance evaluation
Predictive models
recurrent neural network
Recurrent neural networks
Training
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Summary:Diabetes is one of the major health issues that affect more than 10.5 percent of the adult population across the globe. This study applied deep learning techniques of deep belief network (DBN), long short-term memory (LSTM) and recurrent neural network (RNN) with Bayesian optimization on a diabetes dataset to forecast patients with diabetes. A splitting ratio of 80:20 was used for model performance evaluation. DBN model had the lowest mean absolute error in comparison to the other two models with 95.79% accuracy, 0.0331 mean absolute error, 0.0709 mean squared error, 0.1204 loss function, 0.9458 precision, 0.1819 RMSE, and 0.5307 recall. The results from this study validate that the DBN model can be used on a larger dataset to reduce variance and model overfitting, thereby achieving a better accuracy score.
ISSN:2769-5654
DOI:10.1109/CSCI62032.2023.00063