Jorjani Biomedicine Journal

Background and objectives: Investigatingg the mortality in a population has been considered as one of the appropriate methods of health detection. Although, there are some problems such as lack of confidence in accuracy measurement and quality of data collection. Establishment of death registration systems and using international classification codes of diseases, and also mortality data integrating by responsible organizations have solved great parts of the previous problems. In this study, considering a set of parameters, the study population was divided into two groups: deceased under one year (infants) and over one year (adults).  Then both groups were clustered using the K-means method to identify different groups. Hidden models and useful patterns were also discovered using decision tree algorithms. Finally, a neural network algorithm was used to show the ranking of attributes in order of their importance.

Methods: In this research, data of 12,865 deceased individuals in Golestan province since 2007 to 2009 is studied. The data has been obtained from the Health Center of Golestan province. The main characteristics used in this study are: deceased age, gender, cause of death, place of residence and place of death. K-means algorithm is used to cluster data. The decision tree algorithms and neural networks algorithm were also used for classification. Finally, results and rules were extracted. Due to different natures of causes of death in infants and adults, studying on these different groups is performed separately.

Results: In clustering phase, the optimal number of clusters is obtained by Dunn index; eight clusters for infants and seven clusters for adults were obtained. Among four decision-tree algorithms (C5.0, QUEST, CHAID and CART), C5.0 algorithm with high correction rate, 77.37% in infants data and 96.86% in adults data was the best classifier algorithm. Age, gender and place of death were the most important variables that were detected by neural network algorithm.

Conclusion: In the present study, the collected mortality data was clustered by considering the effective factors and the standard of International Classification of Diseases. The hidden patterns of mortality for infants and adults were extracted. Due to the explicit nature and the intelligibility of the decision tree algorithms, the results and extracted rules are very useful for specialists in this field.

Background & Objective: Health databases contain a large amount of clinical data. Investigating the relationships and patterns in these databases can lead to new medical knowledge. Nutrition indicators are designed to evaluate the dietary quality in communities. Metabolic syndrome is a set of risk factors which may increase the risk of heart disease. Inappropriate diet is one of the most important factors in the occurrence of metabolic syndrome. The health industry is constantly producing a large amount of data in medical areas which requires a technique to disclose useful information and important relationships. The aim of this study was to compare the dietary diversity score (DDS) with healthy eating index (HEI) in terms of nutrient intake and assessing the association with metabolic syndrome with the approach of data mining.
Methods: A total of 1019 teenagers between the ages of 11 to 18 years were enrolled in this study.  Data were collected using a past 24-hour food frequency questionnaire (FFQ). Nutrition data collection and determination of anthropometric characteristics and medical examinations were performed in Isfahan Cardiovascular Institute. Data were analyzed by TANAGRA data mining tool.
Results: Statistical, regression and classification techniques were used for data exploration. The average score of DDS was 3.98 ± 1.10, while the HEI average was 59.23 ± 8.84 and the prevalence of metabolic syndrome was 17.39%. The average of DDS provided a better nutritional value in comparison to HEI. HEI was more robust in controlling received energy and carbohydrates. DDS was not significantly correlated with any of the components of metabolic syndrome, while HEI was weakly correlated with high waist circumference. High quartiles of HEI could predict a lower risk of metabolic syndrome, while high quartiles of DDS can predict higher risk of metabolic syndrome.
Conclusion: The findings of this study revealed that the DDS score may result in better nutrition uptake while adhering to the HEI was more effective in reducing the risk of metabolic syndrome.