Diagnosing Metabolic Syndrome Using Genetically Optimised Bayesian ARTMAP
Document Type
Article
Publication Date
1-1-2019
Abstract
Metabolic Syndrome (MetS) constitutes of metabolic abnormalities that lead to non-communicable diseases, such as type II diabetes, cardiovascular diseases, and cancer. Early and accurate diagnosis of this abnormality is required to prevent its further progression to these diseases. This paper aims to diagnose the risk of MetS using a new non-clinical approach called 'genetically optimized Bayesian adaptive resonance theory mapping' (GOBAM). We evolve the Bayesian adaptive resonance theory mapping (BAM) by using genetic algorithm to optimize the parameters of BAM and its training input sequence. We use the GOBAM algorithm to classify individuals as either being at risk of MetS or not at risk of MetS with a related posterior probability, which ranges between 0 and 1. A data set of 11 237 Malaysians from the CLUSTer study stratified by age and gender into four subcategories was used to evaluate the proposed GOBAM algorithm. The comparative evaluation of our results suggested that the GOBAM performs significantly better than other classical adaptive resonance theory mapping models on the area under the receiver operating characteristic curves (AUC) and others criteria. Our algorithm gives an AUC of 86.42 %, 87.04 %, 91.08 %, and 89.24 % for the young female, middle aged female, young male, and middle-aged male subcategories, respectively. The proposed model can be used to support medical practitioners in accurate and early diagnosis of MetS. © 2013 IEEE.
Keywords
adaptive resonance theory, Bayesian ARTMAP, genetic algorithm, Metabolic syndrome
Divisions
fsktm,fac_med
Funders
University of Malaya Postgraduate Research Fund under Project PG038-2016A,UM Grand Challenge Project under Project GC003A-14HTM,Faculty of Information Technology, King Mongkut’s Institute of Technology Ladkrabang
Publication Title
IEEE Access
Volume
7
Publisher
Institute of Electrical and Electronics Engineers