Prediction technology of type II diabetes based on Markov chain

Sen Lin Luo; Wei Dong Guo; Ji Zhang; Feng Guo; Yan Ying Chen; Long Fei Han; Song Jing Chen

Prediction technology of type II diabetes based on Markov chain

Sen Lin Luo, Wei Dong Guo^*, Ji Zhang, Feng Guo, Yan Ying Chen, Long Fei Han, Song Jing Chen

^*Corresponding author for this work

Beijing Institute of Technology

Research output: Contribution to journal › Article › peer-review

Abstract

Diabetes is on the rise worldwide. Diabetes and its associated complications endanger the life of a large number of populations. Therefore, it is critical to predict and prevent the high incidence rate of diabetes. This paper proposes a method of long-term prediction of type II diabetes incidence rate based on the Markov model. This method applies the naive Bayes arithmetic to calculate the probability vector of risk levels, and establishes the predicting model with selected property subsets of GLU, BMI, CHOL, TG, WAIST, SEX, DME and AGE. Compared with the predicted results obtained by using Archimedes model, the proposed method can predict long-term incidence rate of diabetes. The model recommended in this paper is proved to be simple and accurate for predicting the long-term incidence rate of type II diabetes.

Original language	English
Pages (from-to)	1414-1418
Number of pages	5
Journal	Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology
Volume	31
Issue number	12
Publication status	Published - Dec 2011

Keywords

Markov
Prediction
Probability of disease
Type II diabetes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Cite this

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title = "Prediction technology of type II diabetes based on Markov chain",

abstract = "Diabetes is on the rise worldwide. Diabetes and its associated complications endanger the life of a large number of populations. Therefore, it is critical to predict and prevent the high incidence rate of diabetes. This paper proposes a method of long-term prediction of type II diabetes incidence rate based on the Markov model. This method applies the naive Bayes arithmetic to calculate the probability vector of risk levels, and establishes the predicting model with selected property subsets of GLU, BMI, CHOL, TG, WAIST, SEX, DME and AGE. Compared with the predicted results obtained by using Archimedes model, the proposed method can predict long-term incidence rate of diabetes. The model recommended in this paper is proved to be simple and accurate for predicting the long-term incidence rate of type II diabetes.",

keywords = "Markov, Prediction, Probability of disease, Type II diabetes",

author = "Luo, {Sen Lin} and Guo, {Wei Dong} and Ji Zhang and Feng Guo and Chen, {Yan Ying} and Han, {Long Fei} and Chen, {Song Jing}",

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TY - JOUR

T1 - Prediction technology of type II diabetes based on Markov chain

AU - Luo, Sen Lin

AU - Guo, Wei Dong

AU - Zhang, Ji

AU - Guo, Feng

AU - Chen, Yan Ying

AU - Han, Long Fei

AU - Chen, Song Jing

PY - 2011/12

Y1 - 2011/12

N2 - Diabetes is on the rise worldwide. Diabetes and its associated complications endanger the life of a large number of populations. Therefore, it is critical to predict and prevent the high incidence rate of diabetes. This paper proposes a method of long-term prediction of type II diabetes incidence rate based on the Markov model. This method applies the naive Bayes arithmetic to calculate the probability vector of risk levels, and establishes the predicting model with selected property subsets of GLU, BMI, CHOL, TG, WAIST, SEX, DME and AGE. Compared with the predicted results obtained by using Archimedes model, the proposed method can predict long-term incidence rate of diabetes. The model recommended in this paper is proved to be simple and accurate for predicting the long-term incidence rate of type II diabetes.

AB - Diabetes is on the rise worldwide. Diabetes and its associated complications endanger the life of a large number of populations. Therefore, it is critical to predict and prevent the high incidence rate of diabetes. This paper proposes a method of long-term prediction of type II diabetes incidence rate based on the Markov model. This method applies the naive Bayes arithmetic to calculate the probability vector of risk levels, and establishes the predicting model with selected property subsets of GLU, BMI, CHOL, TG, WAIST, SEX, DME and AGE. Compared with the predicted results obtained by using Archimedes model, the proposed method can predict long-term incidence rate of diabetes. The model recommended in this paper is proved to be simple and accurate for predicting the long-term incidence rate of type II diabetes.

KW - Markov

KW - Prediction

KW - Probability of disease

KW - Type II diabetes

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M3 - Article

AN - SCOPUS:84862927297

SN - 1001-0645

VL - 31

SP - 1414

EP - 1418

JO - Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology

JF - Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology

IS - 12

ER -

Prediction technology of type II diabetes based on Markov chain

Abstract

Keywords

UN SDGs

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