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|Title||Blood tumor prediction using data mining techniques|
Healthcare systems generate a huge data collected from medical tests. Data mining is the computing process of discovering patterns in large data sets such as medical examinations. Blood diseases are not an exception; there are many test data can be collected from their patients. In this paper, we applied data mining techniques to discover the relations between blood test characteristics and blood tumor in order to predict the disease in an early stage, which can be used to enhance the curing ability. We conducted experiments in our blood test dataset using three different data mining techniques which are association rules, rule induction and deep learning. The goal of our experiments is to generate models that can distinguish patients with normal blood disease from patients who have blood tumor. We evaluated our results using different metrics applied on real data collected from Gaza European hospital in Palestine. The final results showed that association rules could give us the relationship between blood test characteristics and blood tumor. Also, it demonstrated that deep learning classifiers has the best ability to predict tumor types of blood diseases with an accuracy of 79.45%. Also, rule induction gave us an explanation of rules that describes both tumor in blood and normal hematology.
|Published in||Health Informatics—An International Journal|
|Publisher||Academy and Industry Research Collaboration Center (AIRCC)|
|Item link||Item Link|
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|El-Halees, Alaa M._27.pdf||102.8Kb|