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Showing 3 results for درخت تصمیم

Fatemeh Bagheri, Hakimeh Alizadeh Majd, Zahra Mehrbakhsh, Majid Ziaratban,
Volume 2, Issue 2 (10-2014)
Abstract

Background & Objective: Prediction of health status in newborns and also identification of its affecting factors is of the utmost importance. There are different ways of prediction. In this study, effective models and patterns have been studied using decision tree algorithm. Method: This study was conducted on 1,668 childbirths in three hospitals of Shohada, Omidi and Mehr in city of Behshahr. Variables such as baby's gender, birth weight, birth order, maternal age, maternal history of illness, gestational diseases, type of delivery, reason of caesarean section, maternal age, family relationship of father and mother, mother's blood type, mother's occupation and blood pressure and place of residence were chosen as predictive factors of decision tree categorization method. The health status of the baby was used as a dependent dual-mode variable. All variables were used in clustering and correlation rules. Prediction was done and then compared using 4 decision-tree algorithms. Results: In the clustering method, the optimal number of clusters was determined as 8, using the Dunn index measurement. Among all the implemented algorithms of CART, QUEST, CHAID and C5.0, C5.0 algorithm with detection rate of 94.44% was identified as the best algorithm. By implementing the Apriori algorithm, strong correlation rules were extracted with regard to the threshold for Support and Confidence. Among the characteristics, maternal age, birth weight and reason of caesarean section with the highest impacts were found as the most important factors in the prediction. Conclusion: Due to the simple interpretation of the decision tree and understandability of the extracted rules derived from it, this model can be used for (most individuals) professionals and pregnant women at different levels.
Arezoo Bagheri, Mahsa Saadati,
Volume 3, Issue 2 (10-2015)
Abstract

Background and Objective: Discriminant analysis and logistic regression are classical methods for classifying data in several studies. However, these models do not lead in valid results due to not meeting all necessary assumptions. The purpose of this study was to classify the number of Children Ever Born (CEB) using decision tree model in order to present an efficient method to classify demographic data.

Methods: In the present study, CART tree model with Gini splitting rule was fitted to classify the number of CEB in fertility behavior of at least once married 15-49 year-old women, in Semnan-2012. 405 women aged 15-49 years old comprised the survey sample.

Results: Women in first and second birth cohorts who had married at an early age had 3 CEB while women who had married at an older age had 2 CEB. Women in third birth cohort who had married at an early age and were employed, had 2 CEB while unemployed women in this cohort whose type of marriages were familial and non-familial had 0 and 1 CEB respectively. Women in the third birth cohort who were married in older age had 1 CEB.

Conclusion: Among important advantages of CART model are the simplicity in interpretation, using distribution-free measures, considering missing data and outliers for construction trees which has increased the usage of this method. Therefore, this method is a suitable way for classifying demographic data in comparison to other classical modeling methods in the conditions that necessary assumptions are not met.


Fatemeh Bagheri, , ,
Volume 3, Issue 2 (10-2015)
Abstract

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.



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