An Improved Artificial Neural Network Model for Effective Diabetes Prediction
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. I...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-01-01
|
| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2021/5525271 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849305235407765504 |
|---|---|
| author | Muhammad Mazhar Bukhari Bader Fahad Alkhamees Saddam Hussain Abdu Gumaei Adel Assiri Syed Sajid Ullah |
| author_facet | Muhammad Mazhar Bukhari Bader Fahad Alkhamees Saddam Hussain Abdu Gumaei Adel Assiri Syed Sajid Ullah |
| author_sort | Muhammad Mazhar Bukhari |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-02f1c36a149c4be0b6e7e5ec1a99db78 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-02f1c36a149c4be0b6e7e5ec1a99db782025-08-20T03:55:32ZengWileyComplexity1076-27871099-05262021-01-01202110.1155/2021/55252715525271An Improved Artificial Neural Network Model for Effective Diabetes PredictionMuhammad Mazhar Bukhari0Bader Fahad Alkhamees1Saddam Hussain2Abdu Gumaei3Adel Assiri4Syed Sajid Ullah5Department of Computer Science, National College of Business Administration & Economics, Lahore, PakistanDepartment of Information Systems, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi ArabiaIT Department, Hazara University, Mansehra 21120, KP, PakistanResearch Chair of Pervasive and Mobile Computing, Department of Information Systems, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi ArabiaManagement Information Systems Department, College of Business, King Khalid University, Abha 61421, Saudi ArabiaIT Department, Hazara University, Mansehra 21120, KP, PakistanData 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.http://dx.doi.org/10.1155/2021/5525271 |
| spellingShingle | Muhammad Mazhar Bukhari Bader Fahad Alkhamees Saddam Hussain Abdu Gumaei Adel Assiri Syed Sajid Ullah An Improved Artificial Neural Network Model for Effective Diabetes Prediction Complexity |
| title | An Improved Artificial Neural Network Model for Effective Diabetes Prediction |
| title_full | An Improved Artificial Neural Network Model for Effective Diabetes Prediction |
| title_fullStr | An Improved Artificial Neural Network Model for Effective Diabetes Prediction |
| title_full_unstemmed | An Improved Artificial Neural Network Model for Effective Diabetes Prediction |
| title_short | An Improved Artificial Neural Network Model for Effective Diabetes Prediction |
| title_sort | improved artificial neural network model for effective diabetes prediction |
| url | http://dx.doi.org/10.1155/2021/5525271 |
| work_keys_str_mv | AT muhammadmazharbukhari animprovedartificialneuralnetworkmodelforeffectivediabetesprediction AT baderfahadalkhamees animprovedartificialneuralnetworkmodelforeffectivediabetesprediction AT saddamhussain animprovedartificialneuralnetworkmodelforeffectivediabetesprediction AT abdugumaei animprovedartificialneuralnetworkmodelforeffectivediabetesprediction AT adelassiri animprovedartificialneuralnetworkmodelforeffectivediabetesprediction AT syedsajidullah animprovedartificialneuralnetworkmodelforeffectivediabetesprediction AT muhammadmazharbukhari improvedartificialneuralnetworkmodelforeffectivediabetesprediction AT baderfahadalkhamees improvedartificialneuralnetworkmodelforeffectivediabetesprediction AT saddamhussain improvedartificialneuralnetworkmodelforeffectivediabetesprediction AT abdugumaei improvedartificialneuralnetworkmodelforeffectivediabetesprediction AT adelassiri improvedartificialneuralnetworkmodelforeffectivediabetesprediction AT syedsajidullah improvedartificialneuralnetworkmodelforeffectivediabetesprediction |