Modified Immune Evolutionary Algorithm for Medical Data Clustering and Feature Extraction under Cloud Computing Environment

Yu, Jing; Li, Hang; Liu, Desheng

doi:10.1155/2020/1051394

Cited by 21 publications

(6 citation statements)

References 37 publications

(32 reference statements)

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“…In 2020 Yu et.al. [23] designed medical data clustering and feature extraction by using immune evolutionary algorithm under cloud computing for big data. Here the final results produced the better  ISSN: 2252-8938 accuracy of data classification, improve the performance for medical data.…”

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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“…In 2019, Qingchen Zhang, Zhikui Chen proposed in theirs research paper about Deep learning models for diagnosing spleen and stomach diseases in smart Chinese medicine with cloud computing 34 . In 2020, Jing Yu, Hang Li, and Desheng Liu proposed Modified Immune Evolutionary Algorithm for clustering and feature extraction of Medical Data in Cloud Computing Environment 35 …”

Section: Motivationmentioning

confidence: 99%

Rainfall flow optimization based K‐Means clustering for medical data

Rani

Pravin

2021

Concurrency and Computation

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SummaryIn the present trend, availability of data increases more and more in all the fields in more complex way. It is very difficult for handling in an effective way with best scalability for proper decision making of knowledge extraction. K‐Means algorithm is the most familiar extensive old‐style partitioned and faster clustering algorithm than other clustering methods. But it is very subtle for initial centroids and it can be simply surrounded. So, in need of effective optimum centroid with faster clustering, this paper proposed Rainfall Flow Optimization (RFFO) based on K‐Means algorithm. RFFO is a new flood optimization technique like other optimization methods such as PSO, ACO, BCO, and so forth. RFFO is based on the nature of rainfall flow with various environments and its behavior of flow from shallow to depth. Scientific calculations are also used to find flow of data from one location to another location by its present location of waterfall, its velocity, and its most neighboring flow depth. This water storage mainly depends on nearest storage location, maximum depth, size of the storage area, and condition of the storage location. RFFO has some of the unique features than other optimization methods like speed of flow, total quantity of flow, location of flow, and so forth, that determines the optimal centroid. The performance of this proposed RFFO technique is measured with Accuracy, Jaccard Coefficient, Random Coefficient, and also compared with other existing methods using medical data set to cluster risk factor of heart disease with 300 data set.

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“…Clustering analysis is an unsupervised learning model widely used in data mining ( 18 ) and has been utilized to determine the subtypes of many diseases according to their numerous indicators such as idiopathic inflammatory myopathies ( 19 ), class III malocclusion ( 20 ) and others ( 21 ). However, there was no clustering analysis based on the cephalograms in the research of TMD.…”

Section: Introductionmentioning

confidence: 99%

Development of a new category system for the profile morphology of temporomandibular disorders patients based on cephalograms using cluster analysis

Zhu

Zheng

Zhang

et al. 2022

Front. Public Health

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ObjectiveThis study aims to develop a new category scheme for the profile morphology of temporomandibular disorders (TMDs) based on lateral cephalometric morphology.MethodsFive hundred and one adult patients (91 males and 410 females) with TMD were enrolled in this study. Cluster tendency analysis, principal component analysis and cluster analysis were performed using 36 lateral cephalometric measurements. Classification and regression tree (CART) algorithm was used to construct a binary decision tree based on the clustering results.ResultsTwelve principal components were discovered in the TMD patients and were responsible for 91.2% of the variability. Cluster tendency of cephalometric data from TMD patients were confirmed and three subgroups were revealed by cluster analysis: (a) cluster 1: skeletal class I malocclusion; (b) cluster 2: skeletal class I malocclusion with increased facial height; (c) cluster 3: skeletal class II malocclusion with clockwise rotation of the mandible. Besides, CART model was built and the eight key morphological indicators from the decision tree model were convenient for clinical application, with the prediction accuracy up to 85.4%.ConclusionOur study proposed a novel category system for the profile morphology of TMDs with three subgroups according to the cephalometric morphology, which may supplement the morphological understanding of TMD and benefit the management of the categorical treatment of TMD.

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Modified Immune Evolutionary Algorithm for Medical Data Clustering and Feature Extraction under Cloud Computing Environment

Cited by 21 publications

References 37 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

Rainfall flow optimization based K‐Means clustering for medical data

Development of a new category system for the profile morphology of temporomandibular disorders patients based on cephalograms using cluster analysis

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