An Efficient Human Disease Detection Using Data Mining Techniques

Abstract

The modern life styles leads to many health problems. In traditional health care systems, different doctors analyzing the same set of symptoms of a patient may diagnose it to different diseases. It leads to incorrect disease detection. Here we propose an automated system of an efficient human disease detection using data mining techniques and suitable truth discovery mechanism. The proposed architecture is an efficient system for human disease detection that implements clustering mechanism for categorizing similar symptoms and truth discovery mechanism to resolve the conflict of incorrect disease detection. The performance analysis show that the system achieves 85% of accuracy.