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Table 11 Compare the accuracy of all algorithms on malaria and typhoid datasets

From: Machine learning based efficient prediction of positive cases of waterborne diseases

Model

Accuracy

Comparison of all algorithm results malaria dataset

Decision tree algorithm

Random forest algorithm

Support vector machine algorithm

Logistics Regression algorithm

KNN algorithm

0.5925

0.6003

0.586

0.5864

0.5948

Comparison of all algorithms results typhoid dataset

Decision tree algorithm

Random forest algorithm

Support vector machine algorithm

Logistics Regression algorithm

KNN algorithm

0.6189

0.775

0.6095

0.6152

0.6155