Abstract
Currently, medical data clustering is a very active and effective part of the research area to take proper decisions at the medical field from medical data sets. But medical data clustering is a very challenging issue due to limitless receiving data, vast size, and high frequencies. To achieve this and improve the performance with fast and effective clustering, this paper proposes a hybrid optimization technique, namely, the K-means-based rider sunflower optimization (RSFO) algorithm for medical data. In this research, initially, the data preprocessing phase has been carried out to clean the current input medical data, and then in the second phase, important features are chosen with the help of the Tversky index with holoentropy. Finally, medical data clustering has been carried out by using hybrid K-means-based rider sunflower optimization (RSFO) algorithm. RSFO is designed to produce optimum clustering centroid, which is the combination of two optimization techniques, such as rider optimization algorithm (ROA) and sunflower optimization (SFO). This hybrid algorithm can get the advantages of both K-means and RSFO technique and avoid premature convergence of K-means algorithm and high computation cost of optimization technique. K-Means clustering algorithm is used to cluster the medical data by using an optimum centroid. The efficiency of the proposed K-means-based rider sunflower optimization algorithm is examined with a heart disease data set and analyzed based on three different performance metrics.
Highlights
Today, there are many clustering models under the data mining, which is the sub-branch of artificial intelligence [1].e main objective of the recent researchers is to project and provide the model in a very fast and effective manner for any type of data. is paper is designed based on fast and effective clustering than existing models
1.1. e Major Contribution of the Research Paper. is paper proposed global optimization technique, to find cluster centroid by using RSFO, and the number of clusters is defined by the user. is research work is structured in the following manner: Section 1 explains the basic introduction part, Section 2 gives challenges of medical data clustering, Section 3 explains the proposed method of medical data clustering, Section 4 explains experimental results and discussion with performance metrics, and Section 5 provides the simulation results based on the proposed K-means-based rider sunflower optimization algorithm
Experimental Results and Discussion e experimental results provide for the proposed K-meansbased rider sunflower optimization which is implemented using the programming language of Python 3.8.6 version in the Windows 10 operating system, Intel i5 core processor
Summary
There are many clustering models under the data mining, which is the sub-branch of artificial intelligence [1]. For effective and efficient clustering of medical data, this paper proposed hybrid data clustering, which can avoid the premature convergence of K-means algorithm and can reduce the computation cost of optimization technique. Is proposed model handles three steps, such as data preprocessing, feature selection by Tversky index with holoentropy [14, 15], and the final step of medical data clustering K-means rider sunflower optimization algorithm. Is research work is structured in the following manner: Section 1 explains the basic introduction part, Section 2 gives challenges of medical data clustering, Section 3 explains the proposed method of medical data clustering, Section 4 explains experimental results and discussion with performance metrics, and Section 5 provides the simulation results based on the proposed K-means-based rider sunflower optimization algorithm.
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