Application of Machine Learning in Animal Disease Analysis and Prediction

Author(s): Shuwen Zhang, Qiang Su*, Qin Chen*

Journal Name: Current Bioinformatics

Volume 16 , Issue 7 , 2021


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Abstract:

Major animal diseases pose a great threat to animal husbandry and human beings. With the deepening of globalization and the abundance of data resources, the prediction and analysis of animal diseases by using big data are becoming more and more important. The focus of machine learning is to make computers how to learn from data and use the learned experience to analyze and predict. Firstly, this paper introduces the animal epidemic situation and machine learning. Then it briefly introduces the application of machine learning in animal disease analysis and prediction. Machine learning is mainly divided into supervised learning and unsupervised learning. Supervised learning includes support vector machines, naive bayes, decision trees, random forests, logistic regression, artificial neural networks, deep learning, and AdaBoost. Unsupervised learning has maximum expectation algorithm, principal component analysis hierarchical clustering algorithm and maxent. Through the discussion of this paper, people have a clearer concept of machine learning and an understanding of its application prospect in animal diseases.

Keywords: Machine learning, animal disease, supervised learning, unsupervised learning, prediction, ensemble learning.

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Article Details

VOLUME: 16
ISSUE: 7
Year: 2021
Published on: 28 July, 2020
Page: [972 - 982]
Pages: 11
DOI: 10.2174/1574893615999200728195613
Price: $95

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