Tehran University Medical Journal

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.

Background: Anemia disease is the most common hematological disorder which most often occurs in women. Knowledge discovery from large volumes of data associated with records of the disease can improve medical services quality by data mining The goal of this study was to determining and evaluating the status of anemia using data mining algorithms.

Methods: In this applied study, laboratory and clinical data of the patients with anemia were studied in the population of women. The data have been gathered during a year in the laboratory of Imam Hossein and Shohada-ye Haft-e Tir Hospitals which contains 690 records and 15 laboratory and clinical features of anemia. To discover hidden relationships and structures using k-medoids algorithm the patients were clustered. The Silhouette index was used to determine clustering quality.

Results: The features of red blood cell (RBC), mean corpuscular hemoglobin (MCH), ferritin, gastrointestinal cancer (GI cancer), gastrointestinal surgery (GI surgery) and gastrointestinal infection (GI infection) by clustering have been determined as the most important patients’ features. These patients according to their features have been seg-mented to three clusters. First, the patients were clustered according to all features. The results showed that clustering with all features is not suitable because of weak structure of clustering. Then, each time the clustering was performed with different number of features. The silhouette index average is 80 percent that shows clustering quality. Therefore clustering is acceptable and has a strong structure.

Conclusion: The results showed that clustering with all features is not suitable because of weak structure. Then, each time the clustering was performed with different number of features. The first cluster contains mild iron deficiency anemia, the second cluster contains severe iron deficiency anemia patients and the third cluster contains patients with other anemia cause.