Applications of Self-Organising Map (SOM) for prioritisation of endemic zones of filariasis in Andhra Pradesh, India

Entomological and epidemiological data of Lymphatic Filariasis (LF) was collected from 120 villages of four districts of Andhra Pradesh, India. Self-Organising Maps (SOMs), data-mining techniques, was used to classify and prioritise the endemic zones of filariasis. The results show that, SOMs classified all the villages into three major clusters by considering the data of Microfilaria (MF) rate, infection, infectivity rate and Per Man Hour (PMH). By considering the patterns of cluster, appropriate decision can be drawn for each parameter that is responsible for disease transmission of filariasis. Hence, SOM will certainly be a suitable tool for management of filariasis. The detailed application of SOM is discussed in this paper.

[1]  Joseph Y. Lo,et al.  Self-organizing map for cluster analysis of a breast cancer database , 2003, Artif. Intell. Medicine.

[2]  U. Murty,et al.  Prioritization of malaria endemic zones using self-organizing maps in the Manipur state of India , 2008, Informatics for health & social care.

[3]  Pragya Agarwal,et al.  Self-Organising Maps , 2008 .

[4]  K. Ramaiah,et al.  The mosquito problem and type and costs of personal protection measures used in rural and urban communities in Pondicherry region, South India. , 2003, Acta tropica.

[5]  M Pfaff,et al.  Prediction of cardiovascular risk in hemodialysis patients by data mining. , 2004, Methods of information in medicine.

[6]  J.A.F. Costa,et al.  A new tree-structured self-organizing map for data analysis , 2001, IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222).

[7]  Huidong Jin,et al.  An Integrated Self-Organizing Map for the Traveling Salesman Problem , 2001 .

[8]  Samuel Kaski,et al.  Self organization of a massive document collection , 2000, IEEE Trans. Neural Networks Learn. Syst..

[9]  A. Sprague,et al.  A Data Mining System for Infection Control Surveillance , 2000, Methods of Information in Medicine.

[10]  C. Rogier,et al.  Bayesian analysis of an epidemiologic model of Plasmodium falciparum malaria infection in Ndiop, Senegal. , 2000, American journal of epidemiology.

[11]  A. Githeko,et al.  Predicting Malaria Epidemics in the Kenyan Highlands Using Climate Data: A Tool for Decision Makers , 2001 .

[12]  K. Ramaiah,et al.  The economic burden of lymphatic filariasis in India. , 2000, Parasitology today.