Introduction

Malaria, the third leading cause of death attributable to an infectious disease worldwide, has plagued mankind for countless generations. The problem of Malaria is deeply entrenched in more than 90 countries of the world (WHO, 1998) and result in approximately 300 million acute illnesses and at least one million deaths annually (WHO, 1999). India being a tropical country is a malarial paradise with annual burden estimated to be nearly 2 to 2.5 million cases. North-Eastern region of India is in the Indo-Chinese hill zone of Macdonald's classification of stable malaria (MacDonald, 1957) and contributes nearly 9% of total malaria cases in India (Shiv Lal et al, 2000). In this region, perennial transmission of malaria slashes potential economic growth and thus is a major impediment to the overall development and progress of these areas. Despite several anti-malaria programmes, this region has seen little tangible progress in alleviating the burden of malaria (Mohapatra et al, 1998; Sen et al, 1994). Apparently, there are definite inadequacies that continue to dampen the spirit of public health specialists even since the halcyon days of malaria eradication. On closer scrutiny, operational difficulties stemming from the financial constraints and lack of definite knowledge about the malaria transmission trends are hampering the effective malaria control in the North-Eastern region (Mohapatra et al, 2003). Inaccessible areas owing to floods bear the maximum brunt of malaria. Main factors leading to failures in combating malaria in such regions are predominance of Plasmodium falciparum (Sharma et al, 1999, Dev et al, 2003), difficult terrain (Yadava & Sharma, 1995), favorable eco-climatic conditions (Sharma et al, 1996), lack of proper execution of control operations and wide communication gap between health researchers and policy makers. The problem of drug resistance (Kondrashin et al, 1987; Satyanarayana et al, 1991; Mohapatra et al, 2003), exophilic and exophagic vector behavior and high efficiency of vectors (Sharma et al, 1996, Dev et al, 2003) further aggravate the gravity of complex situation. Due to these various factors encountered in the North-Eastern region, malaria continues to present health services with an immensely difficult and complex challenge. Despite committed attempts for widespread implementation of conventional control methods of recognized efficacy, persistent malaria transmission in this region has underlined the need for alternate strategies to tackle the problem. The highly focal nature of malaria requires targeting of interventions to specific regions at appropriate time. Data mining applications have been successfully used in the past for the spatial clustering of endemic zones (Murty and Neelima Arora, 2007 a, b) prediction of disease outbreak (Kumar et al, 2005). CART is an acronym for Classification and Regression Trees, a statistical procedure introduced by Leo Breiman, Jerome Friedman, Richard Olshen, and Charles Stone (1984). CART has been exploited in genomics (Michailidis, and Shedden, 2003; Garcia et al., 2002), proteomics (Clarke et al., 2003), microarray studies (Boulesteix et al., 2003), ecological (De'ath & Fabricius, 2000; Moisen & Frescino, 2002; Koh & Sodhi, 2004;), risk prediction (Gottschalk, et al., 1998; Paul and Munkvold, 1995), epidemiology (Chang et al., 1999) and social studies(Özge et al., 2004). The CART method has also been used to winnow phenotypes (Hermanek 1994; Masic, 1998), symptoms and prognoses for diagnostic characteristics with which to create decision trees to aid in medical diagnoses(Lopez et al.,1999) and therapeutics(Wolfe et al., 2003;.Smolle and Kahofer, 2001). In the current study, we describe association rules derived using Correlation & Regression tree for prediction of Malaria transmission. This approach will translate into most important attributable benefit of reduced disease burden by providing a better understanding of the variability of malaria to program managers and public health workers.

Materials and Methods

Study area

Dataset

Data mining tool

Methodology

Results

CART generated 22 trees having different number of terminal nodes with different relative error (Table 1).

Figure 1

Table 1: Details of trees generated by CART

The optimal tree obtained using LS method possessed 13 terminal nodes with a cross-validated error of 0.56743 ± 0.05412. A node is partitioned in such a way that left child node gets all cases with lower value of the splitting variable. Each decision rule is represented as a terminal node in the tree. The tree was further grown elevating each level at a time for comparison of rules and relative cost. The maximal grown tree showed 23 nodes with a relative cost of 0.567.

The decision rules (IF – THEN) used in this analysis are given in Table 2.

Figure 2

Variable importance of different predictors is summarized in Table3

Figure 3

Table 3: Importance of Predictor variables

Discussion

The disease transmission dynamics is modeled using the parameters such as vector (pathogen transmitting agent) surveillance, parasitic load in the human community and sudden environmental changes. We used data mining tools in CART to find relationships between epidemiological data and the Monthly Parasite Incidence (MPI). These relations are generally hidden in a large dataset. The rules in the CART system are used for the prediction of incidence of Malaria in an effective way.

These observed results could be used as predictive system and also used as a ‘rules-of-thumb-guide' in controlling the transmission of Malaria in a more effective way. The interpretation of the rules is as follows:

For example:

Rule # 1. If RAINFALL= 147.565, RELATIVE HUMIDITY<=89.305, MINIMUM TEMPERATURE<=3.50 C & MONTH= APRIL, AUGUST , FEBRUARY JANUARY , JULY , JUNE , MARCH , MAY THEN MPI=0.257817.

Rule # 22. If RAINFALL >533.55, RELATIVE HUMIDITY>87.75 and MAXIMUM TEMPERATURE> 370 C & MONTH = JUNE, JULY, AUGUST, THEN MPI= 24.9901.

This is in accordance to the general trends that malaria follows, showing very high peaks of incidence in monsoon months. Lowest MPI is predicted to occur when the rainfall limits the availability of surface water required for mosquito breeding and cold temperature limits vector survival, hence showing negligible malaria incidence. While a very high MPI as reflected in rule #22 is expected when optimum temperature is accompanied with significant rainfall amount in Monsoon months.

Results indicate that the 6 predictors, namely, (1) Rainfall (2) Relative Humidity (3) Month,(4)Maximum temperature (5) Rainy Days (6) Minimum temperature influenced the target variable in descending order. This is helpful in ranking the predictor variables. Thus, decision trees play an important role in the management of vector -borne diseases. The above decision rules will be helpful in assessing the parasite incidence in the particular month in any locality. Hence, by observing above decision rules appropriate control measure can be implemented in an appropriate time to reduce the parasite load by the public health officials.

Conclusion

The decision rules obtained by employing CART 5.0 can be used as a prediction tool for any malaria endemic areas in India as well as abroad. The predictive model will have a vital role in estimating the parasite load in the ensuing seasons of study area. Hence, necessary precautionary measures can be undertaken for successful implementation of control strategies. Further, CART 5.0 could rank the predictor variables according to their level of influence on the target variable. From the present study it was observed that RAINFALL, RELATIVE HUMIDITY, MONTH, MAXIMUM TEMPERATURE, RAINY DAYS and MINIMUM TEMPERATURE were found to be influencing the target variable in the descending order. Therefore, it was concluded from this study that data mining tools like CART could be successfully employed for predicting the course of vector-borne diseases.

Acknowledgement