Zonotic Diseases Detection Using Ensemble Machine Learning Algorithms

Abstract

Zonotic diseases are a kind of infectious disease which spreads from animals to humans; the disease usually spreads from infectious agents like virus, prion and bacteria. The identification and controlling the spread of zonotic disease is challenging due to several issues which includes no proper symptoms, signs of zoonoses are very similar, improper vaccination of animals, and poor knowledge among people about animal health. Ensemble machine learning uses multiple machine learning algorithms, to arrive at better performance, compared to individual/stand-alone machine learning algorithms. Some of the potential ensemble learning algorithms like Bayes optimal classifier, bootstrap aggregating (bagging), boosting, Bayesian model averaging, Bayesian model combination, bucket of models, and stacking are helpful in identifying zonotic diseases. Hence, in this chapter, the application of potential ensemble machine learning algorithms in identifying zonotic diseases is discussed with their architecture, advantages, and applications. The efficiency achieved by the considered ensemble machine learning techniques is compared toward the performance metrics, i.e., throughput, execution time, response time, error rate, and learning rate. From the analysis, it is observed that the efficiency achieved by Bayesian model combination, stacking, and Bayesian model combination are high in identifying of the zonotic diseases.