Abstract: Clinical pathology uses laboratory tests on body fluids such as blood and urine to diagnose diseases. Haematology is the study of blood and blood forming organs such as bone marrow. In this study, researchers analyze the components of the bone marrow and the structure of the bone marrow analysis database. The Knowledge Discovery in Databases (KDD) steps are briefly explained. The 18,000 bone marrow analysis records are collected from a reputed hospital and this raw data is transformed into a preprocessed data using the pre-processing phases of KDD such as data cleaning, data selection and data transformation. Eliminate the tuple technique is used to clean the data. The attributes related to the bone marrow components are selected. The ranges of low, high and normal values for the individual attributes are used to transform the data. The data mining techniques are studied and the Apriori algorithm is selected for finding frequent itemsets that are used for the generation of association rules.
D. Minnie and S. Srinivasan, 2014. Preprocessing and Generation of Association Rules for Bone Marrow Analysis Data of Haematology for Acute Myeloid Leukemia. Asian Journal of Information Technology, 13: 29-37.