Now-a-days humankind suffer from many health complications. People are affected by progressive diseases (like as Heart, Diabetes, AIDS, Hepatitis and Fibroid) and their complications. Data mining (also known as knowledge discovery) is the process of summarizing the data into useful information by analyzing data from different perspectives. Data Mining is a technology for processing large volume of data that combines traditional data analysis methods with highly developed algorithms. Data mining techniques can be used to support a wide range of security and business applications such as work flow management, customer profiling and fraud detection. It can be also used to predict the outcome of future observations. Data mining techniques can be developed by the Decision Tree Algorithm. According to a recent survey of the World Health Organization (WHO), all diseases and its complications are problematic health hazards of this century. A better and early diagnosis of disease may improve the lives of all people affected and people may lead healthy lives. In this paper, the authors present the Decision Tree Algorithm for better diagnosis of diseases using Association Rule mining. Using this computational intelligence technique the authors tested the performance of the method using disease data sets. The authors presented a better algorithm which is used to calculate sensitivity, specificity, comprehensibility and rule length. This gain and gain ratio achieved has promising accuracy.