Published Paper Details:

Data Mining is one of the knowledge discovery steps in database, in which modeling techniques are applied. In this research work, the performance analysis of classification algorithms like K - Means and FCM methods are applied for dealing with medical database for mining. To increase the efficiency of mining process, some preprocessing needs to be done to the data. Medicinal data mining methods are used to analyze the medical data information resources. The effort to develop knowledge and experience of frequent specialists and clinical selection data of patients collected in databases to facilitate the diagnosis process is considered a valuable option. Diagnose the Diabetes, Lung and Liver diseases are a significant and tedious task in medicine. For detecting a disease number of tests should be required from the patient. But using data mining technique the number of test should be reduced. This reduced test plays an important role in time and performance. This research work analyzes and performance study about how data mining techniques are used for predicting the diabetes, lung and liver diseases. In this work, use familiar two data mining algorithms and performance evaluation of different UCI Repository datasets like Diabetes, Liver Disorder and Lung Cancer data on the basis of Performance Measure and Cost Measure. Experimental results showed the good accuracy when applied to the adjust data.