Risk Prediction of Diabetes Progression Using Big Data Mining with Multifarious Physical Examination Indicators
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Purpose: The purpose of this study is to explore the independent-influencing factors from normal people to prediabetes and from prediabetes to diabetes and use different prediction models to build diabetes prediction models. Methods: The original data in this retrospective study are collected from the participants who took physical examinations in the Health Management Center of Peking University Shenzhen Hospital. Regression analysis is individually applied between the populations of normal and prediabetes, as well as the populations of prediabetes and diabetes, for feature selection. Afterward,the independent influencing factors mentioned above are used as predictive factors to construct a prediction model. Results: Selecting physical examination indicators for training different ML models through univariate and multivariate logistic regression, the study finds Age, PRO, TP, and ALT are four independent risk factors for normal people to develop prediabetes, and GLB and HDL.C are two independent protective factors, while logistic regression performs best on the testing set (Acc: 0.76, F-measure: 0.74, AUC: 0.78). We also find Age, Gender, BMI, SBP, U.GLU, PRO, ALT, and TG are independent risk factors for prediabetes people to diabetes, and AST is an independent protective factor, while logistic regression performs best on the testing set (Acc: 0.86, F-measure: 0.84, AUC: 0.74). Conclusion: The discussion of the clinical relationships between these indicators and diabetes supports the interpretability of our feature selection. Among four prediction models, the logistic regression model achieved the best performance on the testing set. Keywords: prediabetes, prediction model, physical examination, machine learning, regression analysisKeywords:
Prediabetes
Stepwise regression
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Interpretability
We examined the comparative behavior of subject-specific multivariate and univariate reference regions, using both computer-generated data and serial (semi-annual) measurements of selected analytes in subjects from a large health-maintenance program. Univariate studies under both homeostatic and random-walk time-series models were helpful in defining expected results, but only the homeostatic model was used in multivariate as well as univariate forms. Analysis of the computer-generated data and the real biochemical series produced similar findings, which showed the multivariate subject-specific reference region to be much more conservative than corresponding univariate intervals. That is, a multidimensional point of p correlated observations is quite likely to lie within the individual's multivariate reference region (based on past observation vectors), even when one or more of the observations lie outside their separate reference intervals for that individual. One consequence of this high specificity against univariate false positives in a large surveillance program is a higher than expected proportion of positive multivariate vectors in which none of the values lie outside their univariate ranges. Thus, although the development of multivariate reference regions should be encouraged, they should be used in conjunction with, not instead of, univariate ranges.
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This paper generalises four types of disturbance commonly used in univariate time series analysis to the multivariate case, highlights the differences between univariate and multivariate outliers, and investigates dynamic effects of a multivariate outlier on individual components. The effect of a multivariate outlier depends not only on its size and the underlying model, but also on the interaction between the size and the dynamic structure of the model. The latter factor does not appear in the univariate case. A multivariate outlier can introduce various types of outlier for the marginal component models. By comparing and contrasting results of univariate and multivariate outlier detections, one can gain insights into the characteristics of an outlier. We use real examples to demonstrate the proposed analysis.
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ABSTRACT: Multivariate methods of trend analysis offer the potential for higher power in detecting gradual water quality changes as compared to multiple applications of univariate tests. Simulation experiments were used to investigate the power advantages of multivariate methods for both linear model and Mann‐Kendall based approaches. The experiments focused on quarterly observations of three water quality variables with no serial correlation and with several different intervariable correlation structures. The multivariate methods were generally more powerful than the univariate methods, offering the greatest advantage in situations where water quality variables were positively correlated with trends in opposing directions. For illustration, both the univariate and multivariate versions of the Mann‐Kendall based tests were applied to case study data from several lakes in Maine and New York which have been sampled as part of EPA's long term monitoring study of acid precipitation effects.
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In this paper, we propose and study new multivariate extensions of the dispersive, right‐spread, decreasing mean residual life and new better than used in expectation univariate orders. These new orders are based on the comparison of univariate marginal distributions conditional on survival data for the rest of the components. Relationships among multivariate orders and applications to some multivariate random vectors are also provided. Copyright © 2011 John Wiley & Sons, Ltd.
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Abstract We compare univariate and multivariate forecasts based on ARMA models. In theory we cannot do worse by using a multivariate model instead of a univariate one, but we can risk getting no improvement. Conditions for no improvements are discussed as well as cases where large improvements occur. The effect of estimated parameters is examined and found to be small granted that a good method of estimation is used. However, multivariate models could be very sensitive to structural changes. This is illustrated via an example involving monetary data, where the multivariate forecasts perform considerably worse than the univariate ones. This seems to put a limitation on the use of multivariate ARMA forecasting models.
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Aim: Predict the Human Development Index (HDI) of 2013 and 2014 of Latin American countries through forecast data mining techniques. Methodology: Full stages of Knowledge Discovery in Databases applied in univariate and multivariate time series. For the prediction, the predicting abilities of 90 predicting models were tested, distributed in two global multivariate, 44 specific multivariate per country and 44 univariate. The algorithm SMOReg was adopted in the development of models as it presented a better performance among the learning algorithms based on functions tested in the experiment. Results: It was observed that the predictions of the models did not present significant statistical differences from the HDI tendencies disclosed in the last report of the United Nations Development Program. Nevertheless, the global multivariate models presented better quality measures in the predictions. Conclusion: The HDI prediction models used with multivariate time series provide better learning of algorithms with the increase of different univariate historical experiences.
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The use of multivariate techniques in the analysis of multivariate problems is illustrated by comparing the results of univariate and multivariate techniques applied to the problem of establishing the nutritional requirements of, and the acid tolerance differences between maize cultivars. Forty-eight maize cultivars were statistically separated into three groups, tolerant, intermediate and intolerant, using a univariate approach. A principal components analysis was then carried out to study the grouping at a multivariate level. The variates included were grain yield, plant height and ten leaf chemical analyses: Al, Mg, P, Ca, K, Mn, Zn, Fe, N and Cu. A non-hierarchical classification was applied to classify cultivars into the three tolerance classes. The univariate method resulted in different groupings for each variate under study, while the multivariate approach ensured one single classification of all cultivars into the three groups.
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We analyze macroeconomic data using univariate and multivariate forecast combining techniques. We simulate forecast errors with different variance-covariance structures. The simulations are used to compare the performance of univariate and multivariate combining techniques.
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