Use of Hidden Markov Models to Identify Background States Behind Risks of Cerebral Infarction and Ischemic Heart Disease

Hiroshi Morimoto

doi:10.5539/jmr.v9n1p24

Abstract

Cold exposure is often said to trigger the incidence of cerebral infarctions and ischemic heart disease. This association between weather and human health has attracted considerable interest, and has been explored using standard statistical techniques such as regression models. Meteorological factors, such as temperature, are controlled by background systems, notably weather patterns. Therefore, it is reasonable to posit that the incidence of diseases is similarly influenced by a background system. The aim of this paper was to identify and construct these respective background systems. Possible background states or "hidden states", behind the incidence of diseases were derived using the EM and Viterbi algorithms with in the framework of hidden Markov models (HMM). A self-organizing map (SOM) enabled identification of weather patterns, considered as background states behind meteorological factors. These background states were then compared, and the hidden states behind the incidence of diseases were identified by six weather patterns. This finding indicates new evidence of the links between weather and human health, shedding light on the association between changes in the weather and the onset of disease.

Highlights

Several scholars have used standard statistical techniques such as regression models to study the correlation between low temperature and the incidence of disease (e.g., McDonalda, McDonaldb, Bidae, Kallmesb, & Cloft, 2012; Jimenez-Conde, 2008)
A self-organizing map (SOM) enabled identification of weather patterns, considered as background states behind meteorological factors. These background states were compared, and the hidden states behind the incidence of diseases were identified by six weather patterns
A reasonable assumption was that background states could relate to meteorological data such as temperature entailed in weather charts or weather patterns

Summary

Introduction

Several scholars have used standard statistical techniques such as regression models to study the correlation between low temperature and the incidence of disease (e.g., McDonalda, McDonaldb, Bidae, Kallmesb, & Cloft, 2012; Jimenez-Conde, 2008). These studies have directly examined the variables of meteorological factors and the incidence of cerebral infarction for the use of regression models. EM and Viterbi algorithms were performed to explore the possible background states of the incidence of disease. These algorithms were used to construct a hidden Markov model (HMM). A self-organizing map (SOM) was constructed to obtain weather patterns as background states of meteorological data, as this is a versatile method for classifying multidimensional data

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Discussion

Conclusion