Predictive Data Analytics Technique for Optimization of Medical Databases

Chauhan, Ritu; Kumar, Neeraj; Rekapally, Ruchita

doi:10.1007/978-981-13-0589-4_40

Cited by 7 publications

(3 citation statements)

References 11 publications

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“…But this hybrid method does not adapt for multi-objective functions and is not processed for high-frequency data streams [21]. Then in 2019 Chauhan et al given a two-step clustering technique to analyze the patient's disorders by using different variables to and determine the earlier stage of the liver disease from the hidden knowledge [22]. In 2020 Yu et.al.…”

Section: Literature Reviewmentioning

confidence: 99%

Fuzzy C-means clustering on rainfall flow optimization technique for medical data

Rani

Srivenkateswaran²,

Rajasekar³

et al. 2023

IJ-AI

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<span lang="EN-US">Due to various killing diseases in the world, medical data clustering is a very challenging and critical task to handle and to take the proper decision from multidimensional complex data in an effective manner. The most familiar and suitable speedy clustering algorithm is K-means than other traditional clustering approaches. But K-means is extra sensitive for initialization of clustering centroid and it can easily surround. Thus, there is a necessity for faster clustering with an effective optimum clustering centroid. Based on that, this research paper projected an optimization-based clustering by hybrid fuzzy C-means (FCM) clustering on rainfall flow optimization technique (RFFO), which is the normal flow and behavior of rainfall flow from one position to another position. FCM clustering algorithm is used to cluster the given medical data and RFFO is used to produce optimum clustering centroid. Finally, the clustering performance is also measured for the proposed FCM clustering on RFFO technique with the help of accuracy, random coefficient, and Jaccard coefficient for medical data set and find the risk factor of a heart attack.</span>

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Section: Literature Reviewmentioning

confidence: 99%

Fuzzy C-means clustering on rainfall flow optimization technique for medical data

Rani

Srivenkateswaran²,

Rajasekar³

et al. 2023

IJ-AI

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“…But, this proposed algorithm is not considered about high frequency of incoming data streams for updating and maintaining the data clusters. In 2019, Chauhan et al [18] proposed two-step clustering technology for patient's disorder analysis using different data variables for optimal clusters with different shapes and sizes. e main objective of this paper is diagnosis of liver disease at the earlier stage from the hidden knowledge with huge database.…”

Section: E Major Contribution Of the Research Papermentioning

confidence: 99%

Clustering by Hybrid K-Means-Based Rider Sunflower Optimization Algorithm for Medical Data

Rani

Pravin

2022

Advances in Fuzzy Systems

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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.

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“…In addition, this is absolutely justified -because automation mechanisms allow first to reduce the costs of any production, and therefore-to make the company's activities more efficient. [3] Understanding what business automation is and what it gives, we should note the following positive aspects of its introduction:…”

mentioning

confidence: 99%

Application of Medical Information Systems in Education and Research in Medicine

Amantay¹,

Duisebekova²

2020

OF THE NATIONAL ACADEMY OF SCIENCES OF THE REPUBLIC OF KAZAKHS

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Medical information systems (MIS) - in a broad sense, it is a form of organization in medicine, uniting in the process of activities of physicians, mathematicians, technicians with a complex of technical means and providing the collection, storage, processing, analysis and delivery of medical information of various profiles in the process of solving problems, and in the narrow sense - a complex of mathematical and technical means to ensure the collection, storage, processing and issuance of medical information in solving problems of clinical medicine or health. The general commands for planning and implementing a medical information system are very large and rich. There are necessary requirements for personnel, capital, and the organization itself. Strict requirements for high reliability and user utility for a real-time system significantly increase hardware and software requirements. The scale of the information system requires careful time planning and modular implementation.

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Predictive Data Analytics Technique for Optimization of Medical Databases

Cited by 7 publications

References 11 publications

Fuzzy C-means clustering on rainfall flow optimization technique for medical data

Fuzzy C-means clustering on rainfall flow optimization technique for medical data

Clustering by Hybrid K-Means-Based Rider Sunflower Optimization Algorithm for Medical Data

Application of Medical Information Systems in Education and Research in Medicine

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