It is undeniable that laboratory information is important in healthcare in many ways such as management, planning, and quality improvement. Laboratory diagnosis and laboratory results from each patient are organized from every treatment. These data are useful for retrospective study exploring a relationship between laboratory results and diseases. By doing so, it increases efficiency in diagnosis and quality in laboratory report. Our study will utilize J48 algorithm, a data mining technique to predict abnormality in peripheral blood smear from 1,362 students by using 13 data set of hematological parameters gathered from automated blood cell counter. We found that the decision tree which is created from the algorithm can be used as a practical guideline for RBC morphology prediction by using 4 hematological parameters (MCV, MCH, Hct, and RBC). The average prediction of RBC morphology has true positive, false positive, precision, recall, and accuracy of 0. 940, 0.050, 0.945, 0.940, and 0.943, respectively. A newly found paradigm in managing medical laboratory information will be helpful in organizing, researching, and assisting correlation in multiple disciplinary other than medical science which will eventually lead to an improvement in quality of test results and more accurate diagnosis.
Saichanma et al. in REF used data mining technique to predict abnormality in peripheral blood smear from 1,362 students by using 13 data set of hematological parameters gathered from automated blood cell counter.
The Observation Report of Red Blood Cell Morphology in Thailand Teenager by Using Data Mining Technique
Advances in Hematology
Advances in Hematology
Medicine