Prioritization of malaria endemic zones using self-organizing maps in the Manipur state of India.
Due to the availability of a huge amount of epidemiological and public health data that require analysis and interpretation by using appropriate mathematical tools to support the existing method to control the mosquito and mosquito-borne diseases in a more effective way, data-mining tools are used to make sense from the chaos. Using data-mining tools, one can develop predictive models, patterns, association rules, and clusters of diseases, which can help the decision-makers in controlling the diseases. This paper mainly focuses on the applications of data-mining tools that have been used for the first time to prioritize the malaria endemic regions in Manipur state by using Self Organizing Maps (SOM). The SOM results (in two-dimensional images called Kohonen maps) clearly show the visual classification of malaria endemic zones into high, medium and low in the different districts of Manipur, and will be discussed in the paper.
Murty
Upadhyayula Suryanarayana
US
Bioinformatics Group, Biology Division, Indian Institute of Chemical Technology (CSIR), Hyderabad, Andhra Pradesh, India. murty_usn@yahoo.com
Srinivasa Rao
Mutheneni
M
Misra
Sunil
S
D016000:Cluster Analysis; D019353:Endemic Diseases; D006801:Humans; D007194:India / Q000453:epidemiology; D008288:Malaria / Q000453:epidemiology*; D008377:Maps as Topic; D008492:Medical Informatics Computing; D040442:Public Health Informatics