The Modified K-MEANS Algorithm And Its Application To Type-Ⅰ Diabetes Glucose Data Clustering

Most of previous studies were concentrated on data mining algorithms for type 2 diabetes patients. This study aims to design and implement a data mining algorithm to assist doctors to diagnose and analyze type 1 diabetes patients' condition. In order to achieve the aim of this study, data of glucose of the diabetes patients have been collected first. Mainstream data mining algorithms have been then studied and compared through literatures review. K-means algorithm has been initially selected to be applied to deal with diabetes patients'data. However, there are three disadvantages of K-means algorithm:a) the performance of K-means algorithm tightly relies on the order of input data. b) Outliers can decrease the performance of the algorithm. c) The data samples which fall into the overlap are difficult to deal with. Therefore, fuzzy logic has been introduced to collaboratively work with K-means algorithm. Experiments are to be carrying out in order to test and verify the proposed algorithm after the implementation of the software. The proposed algorithm and the software are going to be optimized in the nearly future.