Specifying Latent Factors with a Domain Model for Personal Data Analysis

Tago, Kiichi; Takagi, Kosuke; Ito, Katsuhiko; Jin, Qun

doi:10.1109/dasc/picom/datacom/cyberscitec.2018.00056

Cited by 2 publications

(2 citation statements)

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“…Using To use SEM, we need a domain model that expresses the relationship between explicit variables and latent factors. In our previous study, we defined an activity factor as a latent factor and verified the effectiveness of the domain model on personal health data [21]. In that study, motivation to perform activities was considered a latent factor, and we analyzed how behaviors such as step count were changed by motivation.…”

Section: B Calculating Latent Factor Scores Based On Explicit Variablesmentioning

confidence: 99%

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Detection of Health Abnormality Considering Latent Factors Inducing a Disease

2020

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Underlying latent factors may cause a person to feel unwell. As the influence of the latent factors increases, the person will become sick. It is difficult to directly measure the influence of latent factors on risk degrees. However, early symptoms of a disease may affect vital signs such as body temperature and blood pressure, which may be a result of the influence of the latent factors. Deep learning is often used to predict the onset of a disease owing to its high accuracy. However, the reliability of this method is limited because of its characteristics of a black-box model. In this study, we propose a new approach to detect health abnormality. We regard the degree of influence of latent factors as the risk of disease and detect health abnormality before the onset of the disease. In our approach, we used a combination of Structural Equation Modeling (SEM) and Hidden Markov Model (HMM). First, in SEM, a domain model was created, and the factor score was estimated based on the relationship between latent factors and the explicit variables influenced by the factors. Thereafter, risk degrees were quantified with HMM using the estimated factor scores, and abnormality was identified in terms of risk degree. Finally, our proposed method was compared with three baselines: PCA (principal component analysis)-based approach, deep learning, and no-degree estimation methods. The average recall of our method was 98.75%, almost the same as the baselines, and false positive rate (FPR) was 0.186%, lower than the baselines. In the five-fold cross-validation comparing with no-degree estimation method, the average accuracy and recall of our method were 99.7% and 98.3% respectively, and FPR was 0.045%, all much better than the baseline. Moreover, our approach can make the process of obtaining the result visible and help detect abnormality sensitively by setting the threshold according to the risk degree, which can contribute to early detection of a disease and improve the reliability of abnormality detection as well.

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Section: B Calculating Latent Factor Scores Based On Explicit Variablesmentioning

confidence: 99%

“…In our previous studies, we proposed approaches to estimate latent factors [21] and applied them for abnormality detection [22]. However, risk degree estimation was not performed in these studies.…”

Section: B Analysis For Latent Factormentioning

confidence: 99%