Hypoglycaemia detection using fuzzy inference system with intelligent optimiser

Lai, Jiewen; Leung, F.H.F.; Ling, Sai Ho

doi:10.1016/j.asoc.2013.12.015

Cited by 9 publications

(3 citation statements)

References 23 publications

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“…Ling et al [45] also proposed an alarm system based on the hybrid neural logic network with multiple regression. Lai et al [83] developed a fuzzy inference system for hypoglycemia detection, where the system parameters are optimized through an intelligent optimizer.…”

Section: Discussionmentioning

confidence: 99%

Data-Driven Blood Glucose Pattern Classification and Anomalies Detection: Machine-Learning Applications in Type 1 Diabetes

Woldaregay¹,

Årsand²,

Botsis³

et al. 2019

J Med Internet Res

124

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Background Diabetes mellitus is a chronic metabolic disorder that results in abnormal blood glucose (BG) regulations. The BG level is preferably maintained close to normality through self-management practices, which involves actively tracking BG levels and taking proper actions including adjusting diet and insulin medications. BG anomalies could be defined as any undesirable reading because of either a precisely known reason (normal cause variation) or an unknown reason (special cause variation) to the patient. Recently, machine-learning applications have been widely introduced within diabetes research in general and BG anomaly detection in particular. However, irrespective of their expanding and increasing popularity, there is a lack of up-to-date reviews that materialize the current trends in modeling options and strategies for BG anomaly classification and detection in people with diabetes. Objective This review aimed to identify, assess, and analyze the state-of-the-art machine-learning strategies and their hybrid systems focusing on BG anomaly classification and detection including glycemic variability (GV), hyperglycemia, and hypoglycemia in type 1 diabetes within the context of personalized decision support systems and BG alarm events applications, which are important constituents for optimal diabetes self-management. Methods A rigorous literature search was conducted between September 1 and October 1, 2017, and October 15 and November 5, 2018, through various Web-based databases. Peer-reviewed journals and articles were considered. Information from the selected literature was extracted based on predefined categories, which were based on previous research and further elaborated through brainstorming. Results The initial results were vetted using the title, abstract, and keywords and retrieved 496 papers. After a thorough assessment and screening, 47 articles remained, which were critically analyzed. The interrater agreement was measured using a Cohen kappa test, and disagreements were resolved through discussion. The state-of-the-art classes of machine learning have been developed and tested up to the task and achieved promising performance including artificial neural network, support vector machine, decision tree, genetic algorithm, Gaussian process regression, Bayesian neural network, deep belief network, and others. Conclusions Despite the complexity of BG dynamics, there are many attempts to capture hypoglycemia and hyperglycemia incidences and the extent of an individual’s GV using different approaches. Recently, the advancement of diabetes technologies and continuous accumulation of self-collected health data have paved the way for popularity of machine learning in these tasks. According to the review, most of the identified studies used a theoretical threshold, which suffers from inter- and intrapatient variation. Therefore, future studies should consider the differenc...

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Section: Discussionmentioning

confidence: 99%

Data-Driven Blood Glucose Pattern Classification and Anomalies Detection: Machine-Learning Applications in Type 1 Diabetes

Woldaregay¹,

Årsand²,

Botsis³

et al. 2019

J Med Internet Res

124

View full text Add to dashboard Cite

show abstract

“…Of these, 149 studies were subsequently excluded for various reasons. Specifically, 12 studies [14][15][16][17][18][19][20][21][22][23][24][25] presented insufficient data to allow reproduction of the 2 × 2 contingency table, although data on sensitivity and specificity were presented. We asked the authors of these studies to provide N-totals and N-hypos so that we could calculate the number of true positives, false positives, true negatives, and false negatives.…”

Section: Literature Searchesmentioning

confidence: 99%

Ability of Current Machine Learning Algorithms to Predict and Detect Hypoglycemia in Patients With Diabetes Mellitus: Meta-analysis

et al. 2021

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Background Machine learning (ML) algorithms have been widely introduced to diabetes research including those for the identification of hypoglycemia. Objective The objective of this meta-analysis is to assess the current ability of ML algorithms to detect hypoglycemia (ie, alert to hypoglycemia coinciding with its symptoms) or predict hypoglycemia (ie, alert to hypoglycemia before its symptoms have occurred). Methods Electronic literature searches (from January 1, 1950, to September 14, 2020) were conducted using the Dialog platform that covers 96 databases of peer-reviewed literature. Included studies had to train the ML algorithm in order to build a model to detect or predict hypoglycemia and test its performance. The set of 2 × 2 data (ie, number of true positives, false positives, true negatives, and false negatives) was pooled with a hierarchical summary receiver operating characteristic model. Results A total of 33 studies (14 studies for detecting hypoglycemia and 19 studies for predicting hypoglycemia) were eligible. For detection of hypoglycemia, pooled estimates (95% CI) of sensitivity, specificity, positive likelihood ratio (PLR), and negative likelihood ratio (NLR) were 0.79 (0.75-0.83), 0.80 (0.64-0.91), 8.05 (4.79-13.51), and 0.18 (0.12-0.27), respectively. For prediction of hypoglycemia, pooled estimates (95% CI) were 0.80 (0.72-0.86) for sensitivity, 0.92 (0.87-0.96) for specificity, 10.42 (5.82-18.65) for PLR, and 0.22 (0.15-0.31) for NLR. Conclusions Current ML algorithms have insufficient ability to detect ongoing hypoglycemia and considerate ability to predict impeding hypoglycemia in patients with diabetes mellitus using hypoglycemic drugs with regard to diagnostic tests in accordance with the Users’ Guide to Medical Literature (PLR should be ≥5 and NLR should be ≤0.2 for moderate reliability). However, it should be emphasized that the clinical applicability of these ML algorithms should be evaluated according to patients’ risk profiles such as for hypoglycemia and its associated complications (eg, arrhythmia, neuroglycopenia) as well as the average ability of the ML algorithms. Continued research is required to develop more accurate ML algorithms than those that currently exist and to enhance the feasibility of applying ML in clinical settings. Trial Registration PROSPERO International Prospective Register of Systematic Reviews CRD42020163682; http://www.crd.york.ac.uk/PROSPERO/display_record.php?ID=CRD42020163682

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“…A clustering method partitions based on similarity are defined into equal clusters. Lai et al, [13] proposed a system based on FIS to measure physiological parameters continuously to provide hypoglycaemia detection for Type 1 diabetes mellitus patients. The heart rate, corrected interval of the electrocardiogram signal were among the input of the system that used to detect the hypoglycaemic episodes.…”

Section: Introductionmentioning

confidence: 99%

Identifying Risk Factors of Diabetes using Fuzzy Inference System

Abdullah

2017

IJ-AI

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Identification of the real risk factors of diabetes is still very much inconclusive. In this paper, fuzzy rules based system was devised to identify risk factors of diabetes. The system consists of five input variables: Body Mass Index, age, blood pressure, Creatinine, and serum cholesterol and one output variable: level of risk. Three Gaussian membership functions for linguistic terms are defined for each input variable. The level of risk is defined using three triangular membership functions to represent output variable. Based on the information from patients’ clinical audit reports, the system was used to classify the level of risk of fifty patients that currently undergoing regular diagnosis for diabetes treatment. The system successfully classified the risk into three levels of Low, Medium and High where three main contributing factors toward developing diabetes were identified.<br /> The three risk factors are age, blood pressure and serum cholesterol.<br /> The multi-input system that characterised by IF-THEN fuzzy rules provide easily interpretable result for identifying predictors of diabetes. Research to establish reproducibility and validity of the findings are left for future works.

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Hypoglycaemia detection using fuzzy inference system with intelligent optimiser

Cited by 9 publications

References 23 publications

Data-Driven Blood Glucose Pattern Classification and Anomalies Detection: Machine-Learning Applications in Type 1 Diabetes

Data-Driven Blood Glucose Pattern Classification and Anomalies Detection: Machine-Learning Applications in Type 1 Diabetes

Ability of Current Machine Learning Algorithms to Predict and Detect Hypoglycemia in Patients With Diabetes Mellitus: Meta-analysis

Identifying Risk Factors of Diabetes using Fuzzy Inference System

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