Ensemble Clustering Algorithm with Supervised Classification of Clinical Data for Early Diagnosis of Coronary Artery Disease

Kausar, Noreen; Abdullah, Azween; Belhaouari, Samir Brahim; Palaniappan, Sellappan; Al‐Ghamdi, Bandar; Dey, Nilanjan

doi:10.1166/jmihi.2016.1593

Cited by 39 publications

(21 citation statements)

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“…Furthermore, in case of categorical data, a hyper plane is used to separate the various classes. SVM and Perceptron are the examples of widely used linear Classifiers (Cortes & Vapnik, 1995;Kausar et al, 2016;Shao & Lunetta, 2012). Proximity-based classifiers perform classification by using distance measures.…”

Section: Classificationmentioning

confidence: 99%

Clustering approaches for high‐dimensional databases: A review

Mittal¹,

Goyal

Hemanth

et al. 2019

WIREs Data Min & Knowl

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Data mining is an inevitable task in most of the emerging computing technologies as it debilitates the complexity of datasets by rendering a better insight. Moreover, it entails the efficacy to envisage ingeniously the vast and heterogeneous datasets and thus delineates substantial knowledge from the abundance of data by pragmatic implementation of suitable algorithm. There are galore of algorithms in literature for this purpose. Furthermore, clustering is widely used techniques to analyze the data within the purview of data mining and thus it became as a motivational impetus for the authors to survey the existing literature on this topic rigorously and have consequently identified various key parameters so that concomitant improvement can be possible while selecting a best fit clustering algorithm pertaining to a specific problem domain. Furthermore, clustering, classification and association rule mining are akin and indispensable to data mining and owing to these authors have also included interrelation and intertwining among these terms so that this work will presage chunk of help for the researchers working in this field. The present study also envisages and manifests the challenges associated with the clustering algorithms for two‐ and high‐dimensional databases in a flamboyant fashion. Over and above, this work identifies key parametric attributes to assess the clustering algorithms which in turn benevolent the existing work and paves the way for profound future research in this realm. This article is categorized under: Technologies > Structure Discovery and Clustering Technologies > Classification Technologies > Association Rules Fundamental Concepts of Data and Knowledge > Big Data Mining

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Section: Classificationmentioning

confidence: 99%

Clustering approaches for high‐dimensional databases: A review

Mittal¹,

Goyal

Hemanth

et al. 2019

WIREs Data Min & Knowl

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show abstract

“…According to non-linear techniques, several researchers developed various techniques for MDC. A review of recent techniques [13][14][15][16][17][18] discussed in this section, where the advantage and the drawback of these techniques are presented below.…”

Section: Literature Surveymentioning

confidence: 99%

“…Kausar, A. Abdullah, B. B. Samir, S. Palaniappan, B. S. AlGhamdi, and N. Dey [14] differentiated the normal and abnormal patients by selecting the relevant clinical features using the PCA algorithm. The related parameters and their measures were adjusted and additionally SVM and k-means clustering algorithms were used for classification.…”

Section: Literature Surveymentioning

confidence: 99%

An Effective Fuzzy Neural Network with Reinforcement Learning Approach for Medical Data Classification

Prasad¹,

Francis²,

Huq³

2020

IJIES

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Nowadays, several researchers are facing challenges on the prediction of diseases from the huge volume of medical databases. So, researchers are using data mining techniques like association rules, classification and clustering to address challenges. The physicians make the right decisions for successful diagnosis of various diseases by using the prediction. The existing work classifies the data to predict the certain diseases, but still it faces the difficulties due to overfitting in the training data. The main aim of this research work is to classify the Medical Data (MD) by developing the feature selection based approach in MD Classification as MDC. The irrelevant features are eliminated from the MD by using Recursive Feature Elimination (RFE) method, then ranked the features to reduce the computation cost of the proposed method. The ranked features from the RFE are given as input to the Fuzzy Neural Network (FNN) with Reinforcement Learning (RL), which is used for classification. The proposed RFE-FNN method has the accuracy of the 98.57%, 98.15% sensitivity, 98.64% specificity and 95.47% F-Measure in Heart disease dataset.

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“…Equation [5] represents the fitness function. Now the total of fitness, represented by equation number [6] and the probability value [7] should be calculated [7]. In roulette, wheel approach replacement of candidate is represented by equation [7,8].…”

Section: Endmentioning

confidence: 99%

“…In Coronary Angiography a combination of X-ray and a special dye is used [1,2] for testing the blood flow through the heart [3,4]. Coronary Angiography is done with the cardiac catheterization [5,6]. The cardiac catheterization is a procedure to measure the blood pressure in a heart chamber [7,8].…”

Section: Introductionmentioning

confidence: 99%

Advanced Optimization tool for the Heart Patient

George*,

Tom,

Gaikwad

2019

IJRTE

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Coronary Angiography bypass sugary is meant for removing blockage in the coronary arteries of heart. Removing blockage from the heart with Coronary Angiography leads to the regulation of the blood flow rate at normal pressure. However, after a few years of the Coronary Angiography bypass sugary nearly fifty percent of people will develop again a blockage in the coronary arteries. Although, the Medical Science and Technology advanced so much there is a lack of an Optimization tool that handles complications of the Coronary Angiography bypass sugary. A new Optimization tool using Genetic Algorithm with the Coronary Angiography bypass sugary GA-CABAG, and Cohort Intelligence, Coronary Angiography bypass surgery CI –CABAG is introduced to help to resolve the above-mentioned problem.

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Ensemble Clustering Algorithm with Supervised Classification of Clinical Data for Early Diagnosis of Coronary Artery Disease

Cited by 39 publications

References 0 publications

Clustering approaches for high‐dimensional databases: A review

Clustering approaches for high‐dimensional databases: A review

An Effective Fuzzy Neural Network with Reinforcement Learning Approach for Medical Data Classification

Advanced Optimization tool for the Heart Patient

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