Using decision tree to predict serum ferritin level in women with anemia
Background: Data mining is known as a process of discovering and analysing large amounts of data in order to find meaningful rules and trends. In healthcare, data mining offers numerous opportunities to study the unknown patterns in a data set. These patterns can be used to diagnosis, prognosis and...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | fas |
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Tehran University of Medical Sciences
2016-04-01
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| Series: | Tehran University Medical Journal |
| Subjects: | |
| Online Access: | http://tumj.tums.ac.ir/browse.php?a_code=A-10-25-5434&slc_lang=en&sid=1 |
| _version_ | 1819107926381428736 |
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| author | Parisa Safaee Rassoul Noorossana Kamran Heidari Parya Soleimani |
| author_facet | Parisa Safaee Rassoul Noorossana Kamran Heidari Parya Soleimani |
| author_sort | Parisa Safaee |
| collection | DOAJ |
| description | Background: Data mining is known as a process of discovering and analysing large amounts of data in order to find meaningful rules and trends. In healthcare, data mining offers numerous opportunities to study the unknown patterns in a data set. These patterns can be used to diagnosis, prognosis and treatment of patients by physicians. The main objective of this study was to predict the level of serum ferritin in women with anemia and to specify the basic predictive factors of iron deficiency anemia using data mining techniques.
Methods: In this research 690 patients and 22 variables have been studied in women population with anemia. These data include 11 laboratories and 11 clinical variables of patients related to the patients who have referred to the laboratory of Imam Hossein and Shohada-E- Haft Tir hospitals from April 2013 to April 2014. Decision tree technique has been used to build the model.
Results: The accuracy of the decision tree with all the variables is 75%. Different combinations of variables were examined in order to determine the best model to predict. Regarding the optimum obtained model of the decision tree, the RBC, MCH, MCHC, gastrointestinal cancer and gastrointestinal ulcer were identified as the most important predictive factors. The results indicate if the values of MCV, MCHC and MCH variables are normal and the value of RBC variable is lower than normal limitation, it is diagnosed that the patient is likely 90% iron deficiency anemia.
Conclusion: Regarding the simplicity and the low cost of the complete blood count examination, the model of decision tree was taken into consideration to diagnose iron deficiency anemia in patients. Also the impact of new factors such as gastrointestinal hemorrhoids, gastrointestinal surgeries, different gastrointestinal diseases and gastrointestinal ulcers are considered in this paper while the previous studies have been limited only to assess laboratory variables. The rules of the decision tree model can improve the process of diagnosing and treatment of the patients with iron deficiency anemia and reduce their costs. |
| first_indexed | 2024-12-22T03:01:48Z |
| format | Article |
| id | doaj.art-1af7795b66a0403588abd04a8905c7e5 |
| institution | Directory Open Access Journal |
| issn | 1683-1764 1735-7322 |
| language | fas |
| last_indexed | 2024-12-22T03:01:48Z |
| publishDate | 2016-04-01 |
| publisher | Tehran University of Medical Sciences |
| record_format | Article |
| series | Tehran University Medical Journal |
| spelling | doaj.art-1af7795b66a0403588abd04a8905c7e52022-12-21T18:41:09ZfasTehran University of Medical SciencesTehran University Medical Journal1683-17641735-73222016-04-017415057Using decision tree to predict serum ferritin level in women with anemiaParisa Safaee0Rassoul Noorossana1Kamran Heidari2Parya Soleimani3 Department of Industrial Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran. Department of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran. Department of Emergency Medicine, Loghman Hakim Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran. Department of Industrial Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran. Background: Data mining is known as a process of discovering and analysing large amounts of data in order to find meaningful rules and trends. In healthcare, data mining offers numerous opportunities to study the unknown patterns in a data set. These patterns can be used to diagnosis, prognosis and treatment of patients by physicians. The main objective of this study was to predict the level of serum ferritin in women with anemia and to specify the basic predictive factors of iron deficiency anemia using data mining techniques. Methods: In this research 690 patients and 22 variables have been studied in women population with anemia. These data include 11 laboratories and 11 clinical variables of patients related to the patients who have referred to the laboratory of Imam Hossein and Shohada-E- Haft Tir hospitals from April 2013 to April 2014. Decision tree technique has been used to build the model. Results: The accuracy of the decision tree with all the variables is 75%. Different combinations of variables were examined in order to determine the best model to predict. Regarding the optimum obtained model of the decision tree, the RBC, MCH, MCHC, gastrointestinal cancer and gastrointestinal ulcer were identified as the most important predictive factors. The results indicate if the values of MCV, MCHC and MCH variables are normal and the value of RBC variable is lower than normal limitation, it is diagnosed that the patient is likely 90% iron deficiency anemia. Conclusion: Regarding the simplicity and the low cost of the complete blood count examination, the model of decision tree was taken into consideration to diagnose iron deficiency anemia in patients. Also the impact of new factors such as gastrointestinal hemorrhoids, gastrointestinal surgeries, different gastrointestinal diseases and gastrointestinal ulcers are considered in this paper while the previous studies have been limited only to assess laboratory variables. The rules of the decision tree model can improve the process of diagnosing and treatment of the patients with iron deficiency anemia and reduce their costs.http://tumj.tums.ac.ir/browse.php?a_code=A-10-25-5434&slc_lang=en&sid=1anemia data mining decision trees ferritins. |
| spellingShingle | Parisa Safaee Rassoul Noorossana Kamran Heidari Parya Soleimani Using decision tree to predict serum ferritin level in women with anemia Tehran University Medical Journal anemia data mining decision trees ferritins. |
| title | Using decision tree to predict serum ferritin level in women with anemia |
| title_full | Using decision tree to predict serum ferritin level in women with anemia |
| title_fullStr | Using decision tree to predict serum ferritin level in women with anemia |
| title_full_unstemmed | Using decision tree to predict serum ferritin level in women with anemia |
| title_short | Using decision tree to predict serum ferritin level in women with anemia |
| title_sort | using decision tree to predict serum ferritin level in women with anemia |
| topic | anemia data mining decision trees ferritins. |
| url | http://tumj.tums.ac.ir/browse.php?a_code=A-10-25-5434&slc_lang=en&sid=1 |
| work_keys_str_mv | AT parisasafaee usingdecisiontreetopredictserumferritinlevelinwomenwithanemia AT rassoulnoorossana usingdecisiontreetopredictserumferritinlevelinwomenwithanemia AT kamranheidari usingdecisiontreetopredictserumferritinlevelinwomenwithanemia AT paryasoleimani usingdecisiontreetopredictserumferritinlevelinwomenwithanemia |