Sensitive Region Prediction using Data Mining Technique
Priyanka Pitale1, Asha Ambhaikar2
1Priyanka Pitale, Department of Computer Science, RCET,Bhilai, C.G, india.
2Asha Ambhaikar, Department of Computer Science, RCET,Bhilai, C.G, india.
Manuscript received on May 17, 2012. | Revised Manuscript received on June 22, 2012. | Manuscript published on June 30, 2012. | PP: 332-336 | Volume-1 Issue-5, June 2012. | Retrieval Number: E0532061512/2012©BEIESP

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Abstract: Surveys provide huge amounts of healthcare data which, unfortunately, are not used to discover hidden information for effective decision making. Discovery of hidden patterns and relationships can provide a powerful prediction technique for predicting regions which are sensitive for several diseases. Advanced data mining techniques can help to predict future number of cases of a disease. This research has developed a prototype, Sensitive Region Prediction System (SRPS), using data mining technique, called Linear Regression. Using historical data from various sources such as regional surveys and health reports, it can predict the number of cases of malaria disease. SRPS is user-friendly, platform independent, scalable, portable and expandable. It is implemented on the Java platform. 
Keywords: Decision Making, Hidden Patterns, Java, Linear Regression, SRPS