Survey of Clustering Algorithms for Categorization of Patient Records in Healthcare

Background/Objectives: This research work provides a survey on the various clustering algorithms such as k-means, K Harmonic means and Hybrid Fuzzy K Harmonic Means (HFKHM) for grouping similar items in large dataset. To improve the accuracy of clustering the large dataset HFKHM is used. Methods: The task of analyzing the issues in healthcare databases is extremely difficult since healthcare databases are multi-dimensional, comprising the attributes such as the categorization of tumor, radius, texture, smoothness and compactness of the tumor. This paper presents a related work on the existing clustering algorithms for categorizing the tumors as benign or malignant. Hence clustering algorithms are used to categorize the large dataset based on the diagnosis of the tumor. Findings: The efficiency of the various clustering algorithms is compared based on the accuracy and execution time. K means clustering algorithm produces 88% accuracy, 89% accuracy is obtained with the help of K Harmonic Means clustering approach, 90.5% accuracy is achieved using HFKHM clustering approach. Application: This model can be an efficient approach for categorizing similar patient records based on the symptoms, treatments and age.