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Mining Complete Blood Count Reports For Disease Discovery


Samiullah Jatoi, M. Aamir Panhwar, M. Sulleman Memon, Junaid Ahmed Baloch, and Salahuddin Saddar


Vol. 18  No. 1  pp. 121-127


Healthcare systems create a massive amount of data from medical tests. Data mining is the method to determine patterns in huge data sets such as medical examinations. Blood diseases are not the exception there are many test data that can be collected from their patients. In this research, we have applied data mining technique to discover the core-relationship between Anemia and Thalassemia from Complete Blood Count (CBC)test. The relationship can be exploited to identify and predict the possibility of getting Thalassemia in the patients suffering from Anemia. We have performed experiments using blood test data set collected from Diagnostic and Research Laboratory of LUMHS in Pakistan. Naive Bayesian Network algorithm is used to analyze and evaluate the data set. The Final results show that Bayesian Network has the best capability to predict core-relate between diseases with an accuracy of 98%.


CBC, KNN, Thalassemia, Anemia and Bayesian Network