Blood Disease Detection System Based on Haemogram Report Using Decision Tree Algorithm

There is enormous amount of medical data in healthcare systems gathered from numerous medical tests performed in numerous fields. In order to diagnose diseases early on or even before they manifest, it is still necessary to extract hidden information from medical data. The classifiers use the characteristics of the complete blood count to forecast information about potential blood illnesses in early stages, which may improve the likelihood of a cure. The art of machine learning is in charge of building predictive models based on prior data. The illness prediction procedure may decrease the number of fatalities and improve the quality of life for those tract these diseases. In this study applied decision tree classifier on blood test report dataset and identify various blood diseases like anemia, leukemia, lymphoma, sickle-cell, etc.