Hybrid artificial fish particle swarm optimizer and kernel extreme learning machine for type-II diabetes predictive model

• Published in: Medical & biological engineering & computing Vol. 59; no. 4; pp. 841 - 867
• Main Authors: Kanimozhi, N., Singaravel, G.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.04.2021; Springer Nature B.V
• Subjects: Accuracy; Algorithms; Artificial neural networks; Biomedical and Life Sciences; Biomedical Engineering and Bioengineering; Biomedicine; Classification; Computer Applications; Coronary artery disease; Correlation coefficients; Datasets; Diabetes; Diabetes mellitus; Diabetic neuropathy; Diabetic retinopathy; Heart diseases; Human Physiology; Imaging; Kernels; Kidney diseases; Learning algorithms; Machine learning; Model testing; Original Article; Particle swarm optimization; Prediction models; Radiology; Research facilities; Retinopathy; Risk; Meta-classifier; Artificial fish swarm algorithm; Type-II diabetes classification; Particle swarm optimization; Kernel extreme learning machine
• ISSN: 0140-0118; 1741-0444; 1741-0444
• DOI: 10.1007/s11517-021-02333-x

The World Health Organization ( WHO) estimated that in 2016, 1.6 million deaths caused were due to diabetes. Precise and on-time diagnosis of type-II diabetes is crucial to reduce the risk of various diseases such as heart disease, stroke, kidney disease, diabetic retinopathy, diabetic neuropathy, and macrovascular problems. The non-invasive methods like machine learning are reliable and efficient in classifying the people subjected to type-II diabetics risk and healthy people into two different categories. This present study aims to develop a stacking-based integrated kernel extreme learning machine (KELM) model for identifying the risk of type-II diabetic patients based on the follow-up time on the diabetes research center dataset. The Pima Indian Diabetic Dataset (PIDD) and a Diabetic Research Center dataset are used in this study. A min-max normalization is used to preprocess the noisy datasets. The Hybrid Particle Swarm Optimization-Artificial Fish Swarm Optimization (HAFPSO) algorithm used satisfies the multi-objective problem by increasing the Classification Accuracy (CA) and decreasing the kernel complexity of the optimal learners (NBC) selected. At last, the model is integrated by utilizing the KELM as a meta-classifier which combines the predictions of the twenty Base Learners as a whole. The proposed classification method helps the clinicians to predict the patients who are at a high risk of type-II diabetes in the future with the highest accuracy of 98.5%. The proposed method is tested with different measures such as accuracy, sensitivity, specificity, Mathews Correlation Coefficient, and Kappa Statistics are calculated. The results obtained show that the KELM-HAFPSO approach is a promising new tool for identifying type-II diabetes. Graphical abstract