Predicting The Type of Malaria Using Classification and Regression Decision Trees

Malaria is an infectious disease infecting 200 - 300 million people annually. Environmental factors such as precipitation, temperature, and humidity can affect its geographical distribution and prevalence. The environmental factors are also effective in the abundance and activity of malaria vectors. The present study aimed at presenting a model to predict the type of malaria. This cross-sectional study was conducted using the data of 285 people referring to a health center in Saravan from June 2009 to December 2016. Clementine 12.0 was used for data analysis. The modeling was done using classification and regression decision trees, chi-squared automatic interaction detector, C 5.0, and neural network algorithms. The accuracy of classification and regression decision trees, chi-squared automatic interaction detector, C5.0, and neural network was 0.7217, 0.6698, 0.6840, and 0.6557, respectively. Classification and regression decision trees performed better than the other algorithms in terms of sensitivity, specificity, accuracy, precision, negative predictive value, and area under the ROC curve. The sensitivity and area under the ROC curve were 0.5787 and 0.66 for classification and regression decision trees. Applying data mining methods for the analysis of malaria’s data can change the current attitude toward malaria type determination. Faster and more precise identification of malaria type helps determine the proper cure and improve the performance of health organizations.