Title of article :
Classification of Death Rate due to Women’s Cancers in Different Countries
Author/Authors :
Farhadian, M Dept. of Biostatistics& Epidemiology - School of Public Health - Hamadan University of Medical Sciences, Hamadan, Iran , Moghimbeigi, A Dept. of Biostatistics& Epidemiology - School of Public Health - Hamadan University of Medical Sciences, Hamadan, Iran , Mahjub, H Research Center for Health Sciences - Department of Epidemiology & Biostatistics - School of Public Health - Hamadan University of Medical Sciences, Hamadan, Iran , Mahjub, H Research Center for Health Sciences - Department of Epidemiology & Biostatistics - School of Public Health - Hamadan University of Medical Sciences, Hamadan, Iran , Poorolajal, J Research Center for Health Sciences - Department of Epidemiology & Biostatistics - School of Public Health - Hamadan University of Medical Sciences, Hamadan, Iran , Sadri, GH Research Center for Health Sciences - Department of Epidemiology & Biostatistics - School of Public Health - Hamadan University of Medical Sciences, Hamadan, Iran
Abstract :
Background: The two most frequently diagnosed cancers among women worldwide are breast and cervical
cancers. The objective of the present study was to classify the different countries based on the death rates from
sex specific cancers.
Methods: In this cross-sectional study, we used dataset regarding death rate from breast, cervical, uterine, and
ovarian cancers in 190 countries worldwide reported by World Health Organization. Normal mixture models
were fitted with different numbers of components to these data. The model’s parameters estimated using the EM
algorithm. Then, appropriate number of components was determined and was selected the best-fit model using
the BIC criteria. Next, model-based clustering was used to allocate the world countries into different clusters
based on the distribution of women's cancers. The MIXMOD program using MATLAB software was used for
data analysis.
Results: The best model selected with four components. Then, countries were allocated into four clusters
including 43 (23%) in the first cluster, 28 (14%) in the second cluster, 75 (39%) in the third cluster, and 44 (24%)
in the fourth cluster. Most countries in South America were to the first cluster. In addition, most countries in
Africa, Central, and Southeast Asia were located to the third cluster. Furthermore, the fourth cluster consisted of
Pacific continent, North America and European countries.
Conclusion: Considering the benefits of clustering based on normal mixture models, it seems that can be
applied this method in wide variety of medical and public heath contexts.
Keywords :
Neoplasm , Finite mixture models , Model-based clustering , BIC criteria
Journal title :
Astroparticle Physics