Graphical User Interface Developed for Diseases Prediction by Mean of Clustering and Apriori Algorithm

摘要: prediction is one of the most important issues that we are facing today. A large number patients struggling for their check up even predictive disease like heart attack possibilities, kidney damage change and possibilities lung problem. All these lies in categories. They need not require very vast analysis if can predict. This Research motivate to develop a console(GUI) on basis data mining which used analyze volumes extracts information be converted useful knowledge. And overall predict patient chances disease. These techniques applied medical research papers mainly concentrated predicting failure, Experimental results will show many rules help best failure helps doctors diagnosis decisions by using A- prior k-mean algorithm. By this algorithm it provide easy efficient way find stage To swamp problem healthcare industry gathers enormous amounts which, grievously, "mined" discover hidden effective decision making. However, there lack tools relationships trends data. So due condition able accurately. making system correct diseases with available paper introducing automated console mean clustering & a-priori web based convenient tool absence diseases. Here, consider almost 200 persons console. Preliminary conclusions shows Keywordsmining, disease, A-prior k- Algorithm.