Machine Learning Algorithms for Anemia Disease Prediction

摘要

The remarkable advances in health industry have led to a significant production of data in everyday life. These data require processing to extract useful information, which can be useful for analysis, prediction, recommendations, and decision making. Data mining and machine learning techniques are used to transform the available data into valuable information. In medical science, disease prediction at the right time is the central problem for professionals for prevention and effective treatment plan. Sometimes, in the absence of accuracy this may lead to death. In this study, we investigate supervised machine learning algorithms-Naive Bayes, random forest, and decision tree algorithm-for prediction of anemia using CBC (complete blood count) data collected from pathology centers. The results show that Naive Bayes technique outperforms in terms of accuracy as compared to C4.5 and random forest.