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    DIAGNOSIS OF METABOLIC SYNDROME USING MACHINE LEARNING, STATISTICAL AND RISK QUANTIFICATION TECHNIQUES: A SYSTEMATIC LITERATURE REVIEW
    (2021-01-01)
    Kakudi, Habeebah Adamu
    ;
    Loo, Chu Kiong
    ;
    Moy, Foong Ming
    ;
    Kau, Lim Chee
    ;
    Pasupa, Kitsuchart
    Metabolic syndrome (MetS), known to substantially lower the quality of life is associated with the increased incidence of non-communicable diseases (NCDs) such as type II diabetes mellitus, cardiovascular diseases and cancer. Evidence suggests that MetS accounts for the highest global mortality rate. For the early and accurate diagnosis of MetS, various statistical and ML techniques have been developed to support its clinical diagnosis. We performed a systematic review to investigate the various statistical and machine learning techniques (ML) that have been used to support the clinical diagnoses of MetS from the earliest studies to December 2020. Published literature relating to statistical and ML techniques for the diagnosis of MetS were identified by searching five major scientific databases: PubMed, Science Direct, IEEE Xplore, ACM digital library, and SpringerLink. Fifty-seven primary studies that met the inclusion criteria were obtained after screening titles, abstracts and full text. Three main types of techniques were identified: statistical (n=10), ML (n=44), and risk quantification (n=3). Standardized Z-score is the only statistical technique identified while the ML techniques include principal component analysis, confirmatory factory analysis, artificial neural networks, multiple logistics regression, decision trees, support vector machines, random forests, and Bayesian networks. The areal similarity degree risk quantification, framingham risk score and simScore were the three risk quantification techniques identified. Evidence suggests that evaluated ML techniques, with accuracy ranging from 75.5% to 98.9%, can more accurately diagnose MetS than both statistical and risk quantification techniques. The standardised Z-score is the most frequent statistical technique identified. However, highlighted proof based on performance measures indicate that the decision tree and artificial neural network ML techniques have the highest predictive performance for the prediction of MetS. Evidence suggests that more accurate diagnosis of MetS is required to evaluate the predictive performance of the statistical and ML techniques.
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    Risk quantif cation of metabolic syndrome with quantum particle swarm optimisation
    (2017-01-01)
    Kakudi, Habeebah A.
    ;
    Loo, Chu Kiong
    ;
    Pasupa, Kitsuchart
    Metabolic syndrome (MetS) is a combination of interrelated risk factors associated with an increased risk of developing type II diabetes Mellitus (T2DM), stroke and cardiovascular diseases (CVD). The economic, social and medical burden coupled with increased morbidity of the aforementioned diseases makes their prevention an active research area. Currently, the traditional method of MetS diagnosis is based on dichotomised definitions provided by various expert health organisations. However, this method is laced with the in-determination of MetS in individuals with borderline risk factor values due to a binary diagnosis and the assumption of equal weighting for all risk factors during diagnosis. The purpose of this paper is to examine the use of the MetS areal similarity degree risk analysis based on weighted radar charts comprising of diagnostic thresholds and risk factor results of an individual. We further enhance this risk quantification method by applying quantum particle swarm optimization to derive the weights. The proposed risk quantification was carried out using a sample of 528 individuals from an examination survey conducted between 2007 and 2014 in Serbia. The results are evaluated with the traditional dichotomised method of MetS diagnosis, in this case the joint interim statement (JIS). The results obtained showed that the proposed risk quantification method outperformed the dichotomised method at diagnosing MetS even in individuals who present risk factor examination values at the threshold borderlines.