Review of efficiency of k-means algorithm on studies related to cardio vascular diseases

Abstract

Medical science industry has huge amount of data, but unfortunately most of this data is not mined to find out hidden information present inside the data. Advanced data mining techniques can be used to discover those entire hidden pattern inside the data. Different models can develop from these techniques which will be useful for doctors to take effective decision. The k-means is the simplest, most commonly used and good behavior clustering algorithm used in many applications. It has been observed that conventional k-means algorithms are sensitive to the initial cluster centers, and tend to be trapped by local optima. This may result in TYPE I error causing potential patients, say, of Cardio vascular Diseases (CVD) unattended. The study aims at using step-wise clustering approach to identify different age groups prone to CVDs for different combinations of variables with k-means algorithm.