Abstract
BackgroundSemisupervised and unsupervised anomaly detection methods have been widely used in various applications to detect anomalous objects from a given data set. Specifically, these methods are popular in the medical domain because of their suitability for applications where there is a lack of a sufficient data set for the other classes. Infection incidence often brings prolonged hyperglycemia and frequent insulin injections in people with type 1 diabetes, which are significant anomalies. Despite these potentials, there have been very few studies that focused on detecting infection incidences in individuals with type 1 diabetes using a dedicated personalized health model.ObjectiveThis study aims to develop a personalized health model that can automatically detect the incidence of infection in people with type 1 diabetes using blood glucose levels and insulin-to-carbohydrate ratio as input variables. The model is expected to detect deviations from the norm because of infection incidences considering elevated blood glucose levels coupled with unusual changes in the insulin-to-carbohydrate ratio.MethodsThree groups of one-class classifiers were trained on target data sets (regular days) and tested on a data set containing both the target and the nontarget (infection days). For comparison, two unsupervised models were also tested. The data set consists of high-precision self-recorded data collected from three real subjects with type 1 diabetes incorporating blood glucose, insulin, diet, and events of infection. The models were evaluated on two groups of data: raw and filtered data and compared based on their performance, computational time, and number of samples required.ResultsThe one-class classifiers achieved excellent performance. In comparison, the unsupervised models suffered from performance degradation mainly because of the atypical nature of the data. Among the one-class classifiers, the boundary and domain-based method produced a better description of the data. Regarding the computational time, nearest neighbor, support vector data description, and self-organizing map took considerable training time, which typically increased as the sample size increased, and only local outlier factor and connectivity-based outlier factor took considerable testing time.ConclusionsWe demonstrated the applicability of one-class classifiers and unsupervised models for the detection of infection incidence in people with type 1 diabetes. In this patient group, detecting infection can provide an opportunity to devise tailored services and also to detect potential public health threats. The proposed approaches achieved excellent performance; in particular, the boundary and domain-based method performed better. Among the respective groups, particular models such as one-class support vector machine, K-nearest neighbor, and K-means achieved excellent performance in all the sample sizes and infection cases. Overall, we foresee that the results could encourage researchers to examine beyond the presented features into other additional features of the self-recorded data, for example, continuous glucose monitoring features and physical activity data, on a large scale.
Highlights
Or novelty detection problem involves identifying the anomalous or novel instances, which exhibit different characteristics, from the rest of the data set and has been widely used in various applications including machine fault and sensor failure detection, prevention of credit card or identity fraud, health and medical diagnostics and monitoring, cyber-intrusion detection, and others [1,2,3,4,5,6,7]
Nearest neighbor, support vector data description, and self-organizing map took considerable training time, which typically increased as the sample size increased, and only local outlier factor and connectivity-based outlier factor took considerable testing time
We demonstrated the applicability of one-class classifiers and unsupervised models for the detection of infection incidence in people with type 1 diabetes
Summary
Or novelty detection problem involves identifying the anomalous or novel instances, which exhibit different characteristics, from the rest of the data set and has been widely used in various applications including machine fault and sensor failure detection, prevention of credit card or identity fraud, health and medical diagnostics and monitoring, cyber-intrusion detection, and others [1,2,3,4,5,6,7]. Assume a health diagnostic and monitoring system that detects health changes in an individual by tracking the individual’s physiological parameters, where the current health status is examined based on a set of parameters, and raises a notification alarm when the individual health deteriorates [12] In such a system, it becomes feasible to rely on a method that can be trained using only the regular or normal day measurements (target days) so as to detect deviation from normality [12,14]. There have been very few studies that focused on detecting infection incidences in individuals with type 1 diabetes using a dedicated personalized health model
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