2015
DOI: 10.5120/21307-4126
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Techniques of Data Mining In Healthcare: A Review

Abstract: Data mining is gaining popularity in disparate research fields due to its boundless applications and approaches to mine the data in an appropriate manner. Owing to the changes, the current world acquiring, it is one of the optimal approach for approximating the nearby future consequences. Along with advanced researches in healthcare monstrous of data are available, but the main difficulty is how to cultivate the existing information into a useful practices. To unfold this hurdle the concept of data mining is t… Show more

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Cited by 61 publications
(35 citation statements)
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References 94 publications
(71 reference statements)
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“…Data generated in this sector includes relevant information about clients including their health care utilization status. The hidden trends, patterns, relationships and knowledge in healthcare data can be discovered using the application of data mining approaches [2,4]. Data mining is a fast-growing field of big data science, sometimes known as knowledge discovery from a database (KDD).…”
Section: Introductionmentioning
confidence: 99%
“…Data generated in this sector includes relevant information about clients including their health care utilization status. The hidden trends, patterns, relationships and knowledge in healthcare data can be discovered using the application of data mining approaches [2,4]. Data mining is a fast-growing field of big data science, sometimes known as knowledge discovery from a database (KDD).…”
Section: Introductionmentioning
confidence: 99%
“…There are various hyperplanes or kernel functions that could be chosen in order to maximum distance between data points of both classes, so that future data points can be classi ed more accurately. The main advantages of SVM are the effectiveness in an N-dimensional space and it is memory e cient because it partition data into training points called support vectors used in the decision function [25,31,32]. A Multilayer Perceptron (MLP) is an arti cial network of neurons called Perceptrons.…”
Section: Methodsmentioning
confidence: 99%
“…The third Module is the Classification of Breast Cancer Detector (CBCD). The third module includes the phase of classifying the breast cancer dataset [29] by using an SVM and a neural network model based on extreme learning capability namely Extreme Learning Machine (ELM) to detect benign or malignant type of cancer mass that in this module, dataset is divided into three groups: training, testing, and validation. The first group is training.…”
Section: B Methodologymentioning
confidence: 99%