Optimised Cluster-based Approach for Healthcare Data Analytics
Data analytics is an intriguing study due to the fact that an enormous volume of healthcare data is being generated by different smart IOT-based health tracking devices, and the Artificial Intelligent-based applications. Data analytic tools and unsupervised techniques combinedly make it possible to find and comprehend hidden patterns in a dataset that may not be visible through simple data display. Grouping of voluminous data objects into homogenous clusters is a crucial operation in soft computing. Choosing the right clustering technique and the correct number of partitions to divide the healthcare data for effective analysis is complicated and challenging most of the time. This research work examines clustering approaches on the healthcare datasets with the optimum K-clusters, in order to perform the analysis of the data. In this work, the K-means clustering method is examined and the silhouette score is computed to estimate the optimal K-value and the quality of the cluster.
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