Paper Info
Title
Artificial Bee Colony And Deep Neural Network-Based Diagnostic Model For Improving The Prediction Accuracy Of Diabetes
Abstract
A large number of machine learning approaches are implemented in healthcare field for effective diagnosis and prediction of different diseases. The aim of these machine learning approaches is to build automated diagnostic tool for helping the physician as well as monitor the health status of patients. These diagnostic tools are widely adopted in intensive care unit for life expectancy of patients. In this study, an effort is made to design an automated diagnostic model for the diagnosis and prediction of diabetes patients. The proposed diagnostic model is designed using artificial bee colony (ABC) algorithm and deep neural network (DNN) technique, called ABC-DNN-based diagnostic model. The ABC algorithm is applied to determine the relevant features for diabetes prediction and diagnosis while DNN technique is adopted for the prediction and diagnosis of diabetes affected patients. The performance of proposed diagnostic model is tested over Pima Indian Diabetes dataset and evaluated using accuracy, sensitivity, specificity, F-measure, Kappa, and area under curve (AUC) parameters. Further, 10-fold and 50-50% training-testing method are considered to assess the performance of proposed diagnostic model. The experimental results of proposed ABC-DNN model is compared with DNN technique and several existing diabetes studies. It is observed that proposed ABC-DNN model achieves 94.74% accuracy rate using 10-fold method.
Year
DOI
Venue
2021
10.4018/IJEHMC.2021030102
INTERNATIONAL JOURNAL OF E-HEALTH AND MEDICAL COMMUNICATIONS
Keywords
DocType
Volume
Artificial Bee Colony, Deep Neural Network, Diagnosis, Diagnostic Model, Feature Selection
Journal
12
Issue
ISSN
Citations 
2
1947-315X
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Anand Kumar Srivastava100.34
Yugal Kumar200.68
Pradeep Kumar Singh300.34