Diagnosing Metabolic Syndrome Using Genetically Optimised Bayesian ARTMAP

Kakudi, Habeebah Adamu; Loo, Chu Kiong; Moy, Foong Ming; Masuyama, Naoki; Pasupa, Kitsuchart

doi:10.1109/access.2018.2880224

Cited by 4 publications

(9 citation statements)

References 87 publications

(95 reference statements)

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“…Kakudi et al [62] developed a novel ARTMAP algorithm - the Genetically-Optimized Bayesian ARTMAP (GOBAM) for the early diagnosis and risk prediction of MetS in patients who present with borderline MetSR-F measuremente. ARTMAP is an adaptive artificial system capable of solving the stability-plasticity delima which leads to catastrophic forgetting.…”

Section: What Is the Current State Of Art In Non-clinical Methods Formentioning

confidence: 99%

“…The links are represented as matrix connections that preserver nonlinear associations between attributes[101]. The results of the study showed that there is a strong positive relationship between the diagnosis of MetS, work-related factors and psycho-pathological variables in men and women.Kakudi et al[62] developed a novel ARTMAP algorithm -the Genetically-Optimized Bayesian ARTMAP (GOBAM) for the early diagnosis and risk prediction of MetS in patients who present with borderline MetSR-F measuremente. ARTMAP is an adaptive artificial system capable of solving the stability-plasticity delima which leads to catastrophic forgetting.…”

mentioning

confidence: 99%

“…ARTMAP is an adaptive artificial system capable of solving the stability-plasticity delima which leads to catastrophic forgetting. In[62], the training sequence and parameters of the Bayesian ARTMAP (BAM) are optimised using genetic algorithm (GA). The GA is utilised to search for best combination of parameter values and sample sequence during training.Kakudi et al[62] developed an intelligent model using GOBAM to diagnose the risk of MetS for enhancing the quality of life of patients.…”

mentioning

confidence: 99%

“…In[62], the training sequence and parameters of the Bayesian ARTMAP (BAM) are optimised using genetic algorithm (GA). The GA is utilised to search for best combination of parameter values and sample sequence during training.Kakudi et al[62] developed an intelligent model using GOBAM to diagnose the risk of MetS for enhancing the quality of life of patients. The predictive performance of GOBAM when compared with three other variants of ARTMAP (fuzzy ARTMAP (FAM), GA-FAM and BAM) outperformed all the ARTMAP variants with accuracy and sensitivity and ranging from 85% -90%, and 91% -95% respectively.…”

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confidence: 99%

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Diagnosis of Metabolic Syndrome using Machine Learning, Statistical and Risk Quantification Techniques: A Systematic Literature Review

Kakudi

Loo

Moy

2020

Preprint

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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 January 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-three 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=40), 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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Section: What Is the Current State Of Art In Non-clinical Methods Formentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Diagnosis of Metabolic Syndrome using Machine Learning, Statistical and Risk Quantification Techniques: A Systematic Literature Review

Kakudi

Loo

Moy

2020

Preprint

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“…Eight studies [56], [57], [58], [59], [60], [61], [63], [64] applied ANN in their MetS prediction. Four studies ( [59], [60], [57], [61]) developed various ANN network models trained with the back propagation (BkP) algorithm to predict MetS.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Diagnosis of Metabolic Syndrome Using Machine Learning, Statistical and Risk Quantification Techniques: A Systematic Literature Review

Kakudi

Loo

Moy

et al. 2021

MJCS

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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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Emotion Recognition Using Explainable Genetically Optimized Fuzzy ART Ensembles

2021

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There is a growing demand for explainability in complex artificial intelligence solutions to support critical applications' decision-making processes. Barriers to explainable processes include blackbox classifiers, such as deep learning, and noisy datasets. Affect recognition involving neural networks attempts to map complex human emotions onto Arousal and Valence scales based on physiological signal measurements. Datasets collected for this purpose are inherently noisy and may contain outliers and imbalanced classes, hindering accurate classification. In our approach, these issues are addressed using Fuzzy ART (FA) for clustering data samples into more condensed memory templates, introducing stochastic resonant noise to amplify signal-to-noise ratio, and SMOTE sampling to generate synthetic minority samples. A genetic algorithm is developed for FA optimization and ensemble model selection. Clusters obtained from the resulting ensembles are then used to train an ensemble of boosted decision trees for classification and to visualize the decision-making processes. Individual features such as heart rate variability and EEG band power, as well as feature interactions between pairs of features, may contain critical information as human affect indicators. Contributions of individual features and feature interactions toward describing human affect are quantified and interpreted using Shapley additive explanation values. Three established affect recognition datasets were considered for mapping physiological features onto a binary classification of Low/High Arousal and Positive/Negative Valence. Our framework was able to achieve good generalization for both classification tasks as well as provide detailed insights into the contributions of physiological features towards describing Arousal and Valence affects.

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Diagnosing Metabolic Syndrome Using Genetically Optimised Bayesian ARTMAP

Cited by 4 publications

References 87 publications

Diagnosis of Metabolic Syndrome using Machine Learning, Statistical and Risk Quantification Techniques: A Systematic Literature Review

Diagnosis of Metabolic Syndrome using Machine Learning, Statistical and Risk Quantification Techniques: A Systematic Literature Review

Diagnosis of Metabolic Syndrome Using Machine Learning, Statistical and Risk Quantification Techniques: A Systematic Literature Review

Emotion Recognition Using Explainable Genetically Optimized Fuzzy ART Ensembles

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