Evaluating the Performance of Machine Learning Methods in CPV Prediction: Big Data Application Wıih Different Regions and Parameters

Abstract

Scopa: In the project, machine learning methods that can interpret health data have been applied specifically to Canine parvovirus infection. While CPV can be diagnosed based on clinical findings, it needs to be supported by laboratory findings to distinguish it from other infections. Correct diagnosis is vital to distinguish CPV from other infections with bloody diarrhoea, which can result in death in puppies. For this reason, by analysing the virus together with other data that may be affected by the virus, the methods of making the most accurate decision were compared and evaluated.

Purpose: In this study, it was aimed to interpret CPV, which is considered to be one of the most important infectious agents of dogs, popularly known as mad-head disease, using K-NearestNeighbour (KNN), RandomForest (RF), Logistic Regression and NaiveBayes classification algorithms in terms of different parameters. When the total accuracy values were examined, the accuracy rates decreased in logistic regression and RF methods when the insignificant variable was removed in the model.

Result: RF method made the best predictions when Platelets, Platelet (PLT) variable was in the model.In cases where we do not want to remove this variable from the model, it can give us very efficient results. KNN method gives better results when the number of variables decreases. Especially when the data size increases, it has been observed that the machine learning method gives more efficient results with better performance.

Keywords: Big Data, Data Mining, Big Data in Health, Prediction Algorithms, Canine parvovirus (CPV)