Application of Correlation & Regression Tree (CART) for management of Malaria in Arunachal Pradesh, India

Abstract

Malaria is a focal disease with multitudinous variations in its epidemiological pattern in relation to topographical features. The present paper demonstrates the application of CART (Classification & Regression Trees) for control of malaria in Arunachal Pradesh, India. Baseline epidemiological data of 12 districts of Arunachal Pradesh was employed for deriving prediction rules. The data was categorized into 2 different aspects, namely (1) Epidemiological (2) Meteorological. The intricate and complex interactions that exist between diverse input data sets, as they relate to the target features, are learned and modeled through exhaustive analysis. Predictor variables (maximum temperature, minimum temperature, rainfall, relative humidity, number of rainy days and month) were ranked by CART according to their influence on the target variable (MPI). Application of these easily conceptualized rules, rather than more abstract epidemiological principles, enables even non-specialists to gain an understanding of the malaria problem and in forecasting the malaria transmission dynamics to formulate the intervention strategies to combat malaria effectively.