Diseases are the major cause of death and its occurrence increased drastically. Many medical technologies are available for prediction of diseases. In order to analyze, predict and classify the diseases effectively, data mining tools can be used. This work proposes mining of the death causing diseases based on the age with the help of patient’s record. For this purpose two different data mining algorithms like classification and clustering are chosen. The two classification algorithm used in this work are decision tree and naïve bayes and the two clustering algorithms that have been chosen are k-means clustering and fuzzy c-means clustering algorithms. The data used in this work is collected from 2000 patient’s record which includes the age of the patient and the diseases from which they got affected. The results are obtained in two different ways such as by using classification algorithm and by using clustering algorithm. The performance of classification and clustering algorithms are also analyzed in this work which provides additional information about which algorithm gives the accurate result for this type of data set. The results obtained through this work can be used in various medical fields for disease detection and prevention which may help to minimize the ratio of the death.