title: Classification of Death Rate due to Women's Cancers in Different Countries.
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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Dept. of Biostatistics & Epidemiology, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran.
authors.forename: M
authors.initials: M
authors.lastname: Farhadian
authors.forename: H
authors.initials: H
authors.lastname: Mahjub
authors.forename: A
authors.initials: A
authors.lastname: Moghimbeigi
authors.forename: J
authors.initials: J
authors.lastname: Poorolajal
authors.forename: Gh
authors.initials: G
authors.lastname: Sadri
keywords: BIC criteria; Finite mixture models; Model-based clustering; Neoplasm