Impact Analysis of the Complete Blood Count Parameter using Naive Bayes

Complete Blood Count (CBC) test is considered as a very important test to evaluate the overall health of a person. It plays a significant role in finding an infection, leukaemia, cancer, anaemia, blood count, deficiency of vitamins and minerals, etc. Gujarat Vidyapith is a deemed to be university in which newly admitted students are medically checked up. Here, the health condition of students, proper diagnosis and appropriate treatments are taken care of. CBC test is considered as one of the most important sources for health analysis.However, there are many other variability and confidentiality in the CBC test, which is difficult to be evaluated directly using Machine Learning Algorithm. Therefore, a model is developed in this paper using the Naive Bayes Classifier which can predict Anemia by analyzing the probabilities of effects of various parameters of the CBC test. It will help to detect anemia at an early stage so that the probability of serious complications can be reduced. Students can take precautions to improve their health standards. Ultimately, it will affect the level of concentration, regularity and mental health of students. Therefore, the level of education can be improved.