An Improved Artificial Neural Network Model for Effective Diabetes Prediction
| Autor: | Abdu Gumaei, Syed Sajid Ullah, Saddam Hussain, Muhammad Mazhar Bukhari, Bader Fahad Alkhamees, Adel Saad Assiri |
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| Rok vydání: | 2021 |
| Předmět: |
Decision support system
Article Subject General Computer Science Mean squared error Computer science PID controller Image processing 02 engineering and technology Machine learning computer.software_genre 01 natural sciences Set (abstract data type) 0202 electrical engineering electronic engineering information engineering Multidisciplinary Artificial neural network business.industry 010401 analytical chemistry QA75.5-76.95 Backpropagation 0104 chemical sciences Electronic computers. Computer science Data analysis 020201 artificial intelligence & image processing Artificial intelligence business computer |
| Zdroj: | Complexity, Vol 2021 (2021) |
| ISSN: | 1099-0526 1076-2787 |
| Popis: | Data analytics, machine intelligence, and other cognitive algorithms have been employed in predicting various types of diseases in health care. The revolution of artificial neural networks (ANNs) in the medical discipline emerged for data-driven applications, particularly in the healthcare domain. It ranges from diagnosis of various diseases, medical image processing, decision support system (DSS), and disease prediction. The intention of conducting the research is to ascertain the impact of parameters on diabetes data to predict whether a particular patient has a disease or not. This paper develops an improved ANN model trained using an artificial backpropagation scaled conjugate gradient neural network (ABP-SCGNN) algorithm to predict diabetes effectively. For validating the performance of the proposed model, we conduct a large set of experiments on a Pima Indian Diabetes (PID) dataset using accuracy and mean squared error (MSE) as evaluation metrics. We use different number of neurons in the hidden layer, ranging from 5 to 50, to train the ANN models. The experimental results show that the ABP-SCGNN model, containing 20 neurons, attains 93% accuracy on the validation set, which is higher than using the other ANNs models. This result confirms the model’s effectiveness and efficiency in predicting diabetes disease from the required data attributes. |
| Databáze: | OpenAIRE |
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