Anemia detection using ensemble learning techniques and statistical models

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Computer aided diagnosis of diseases is less costly, time saving, accurate and it eliminates the need of extra manpower in medical decision making. Many of the surveys related to nutrition reveal that almost quarter of the world's population is anemic. Hence there is an earnest need to develop an efficient machine learning classifier that can detect and classify anemia accurately. In this paper five ensemble learning methods : Stacking, Bagging, Voting, Adaboost and Bayesian Boosting are applied on four classifiers : Decision Tree, Artificial Neural Network, Naive Bayes and K-Nearest Neighbor. The aim is to determine which individual classifier or subset of classifier combination achieves maximum accuracy in Red blood cell classification for anemia detection. From the results it is evident that amongst the ensemble methods, stacking ensemble method achieves the highest accuracy. Amongst the individual classifier the Artificial Neural Network performs the best and K-Nearest Neighbor performs the worst. However the classifier combination Decision Tree and K-Nearest Neighbor when applied on Stacking ensemble, achieves an accuracy much higher than the Artificial Neural Network. This indicates an ensemble of classifiers achieves much higher accuracy than individual classifiers. Hence to achieve maximum accuracy in medical decision making an ensemble of classifiers should be used.