MACHINE LEARNING ALGORITHMS FOR ANEMIA DISEASE PREDICTION - A REVIEW









Abstract

Remarkable advances in the healthcare industry are generating important data in our daily lives. This data needs to be processed to extract useful information that may be useful for analysis, prediction, recommendations, and decision making. Transform available data into valuable information using data mining and machine learning techniques. In medicine, timely disease prediction is a central issue for professionals for prevention and effective treatment planning. From time to time, a lack of accuracy can be fatal. This study examines monitored simple Bayes, random forest, and decision tree machine learning algorithms for predicting anemia using CBC (Complete Blood Count) data collected from the Pathology Center. The results show that the Naive Bayes method is superior in accuracy compared to C4.5 and Random Forest.


Modules


Algorithms


Software And Hardware