A COMPUTATIONAL INTELLIGENCE TECHNIQUE FOR EFFECTIVE MEDICAL DIAGNOSIS USING DECISION TREE ALGORITHM (original) (raw)

Now-a-day's humankind suffering with many health complications. This century's the people affected by most progressive diseases (like as Heart disease, Diabetes disease, AIDS disease, Hepatitis disease and Fibroid diseases) and its complications. Data mining (also called 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. The Data mining techniques can be developed by the Decision tree algorithm. According to recent survey of World Health Organization (WHO), all diseases and its complications are problematical 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 life. In this paper, the authors present the Decision tree algorithm for better diagnosis of Diseases using association rule mining. In this computational intelligence techniques 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 for promising accuracy.