An Adaptive parameter free data mining approach for healthcare application

Abstract

Abstract-In today's world, healthcare is the most important factor affecting human life. Due to heavy work load it is not possible for personal healthcare. The proposed system acts as a preventive measure for determining whether a person is fit or unfit based on his/her historical and real time data by applying clustering algorithms viz. K-means and D-stream. Both clustering algorithms are applied on patient's biomedical historical database. To check the correctness of both the algorithms, we apply them on patient's current biomedical data. The Densitybased clustering algorithm i.e. the D-stream algorithm overcomes drawbacks of K-means algorithm.By calculating their performance measures we finally find out effectiveness and efficiency of both the algorithms.

Electronic Health Records: Applications, Techniques and Challenges

“…Patil et al [29] applied D-stream and K-means classification algorithms to implement a system that predicts the patient's current health condition based on the past and real time data of a patient. The results of their system showed that D-stream algorithm improved the performance and its results are more accurate than the other tested algorithm, and therefore it solves the problems of K-means algorithm.…”