A hybrid intelligent model of analyzing clinical breast cancer data using clustering techniques with feature selection

Chen, Chien‐Hsing

doi:10.1016/j.asoc.2013.10.024

Cited by 66 publications

(30 citation statements)

References 26 publications

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“…In addition, biased random-key genetic data clustering algorithm has been proposed by Festa et al [12], which is relatively useful than other related methods. Chen et al [13] proposed a hybrid intelligent model that is efficient in feature selection which has been used to analyze the clinical breast cancer data. Wei et al [14] proposed a novel clustering algorithm for DNA sequence classification and their relationship using a novel clustering algorithm.…”

Section: Introductionmentioning

confidence: 99%

Analysis of k-means clustering approach on the breast cancer Wisconsin dataset

2016

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Section: Introductionmentioning

confidence: 99%

Analysis of k-means clustering approach on the breast cancer Wisconsin dataset

2016

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“…They reported a classification accuracy of 98.84% for breast cancer diagnosis. Chen (2014) presented a hybrid intelligent model for breast cancer diagnoses that can work in the absence of labeled training data. Hence, this work studies the feature selection methods in unsupervised learning models.…”

Section: Related Workmentioning

confidence: 99%

A fuzzy-rough nearest neighbor classifier combined with consistency-based subset evaluation and instance selection for automated diagnosis of breast cancer

Onan

2015

Expert Systems with Applications

182

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“…Although unsupervised techniques have been used in diagnostic many diseases [7]. One unsupervised technique is clustering [8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

RB-Bayes algorithm for the prediction of diabetic in Pima Indian dataset

Rajni

Amandeep

2019

IJECE

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<p>Diabetes is a major concern all over the world. It is increasing at a fast pace. People can avoid diabetes at an early stage without any test. The goal of this paper is to predict the probability of whether the person has a risk of diabetes or not at an early stage. This would lead to having a great impact on their quality of human life. The datasets are Pima Indians diabetes and Cleveland coronary illness and consist of 768 records. Though there are a number of solutions available for information extraction from a huge datasets and to predict the possibility of having diabetes, but the accuracy of their mining process is far from accurate. For achieving highest accuracy, the issue of zero probability which is generally faced by naïve bayes analysis needs to be addressed suitably. The proposed framework RB-Bayes aims to extract the required information with high accuracy that could survive the problem of zero probability and also configure accuracy with other methods like Support Vector Machine, Naive Bayes, and K Nearest Neighbor. We calculated mean to handle missing data and calculated probability for yes (positive) and no (negative). The highest value between yes and no decide the value for the tuple. It is mostly used in text classification. The outcomes on Pima Indian diabetes dataset demonstrate that the proposed methodology enhances the precision as a contrast with other regulated procedures. The accuracy of the proposed methodology large dataset is 72.9%.</p>

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A hybrid intelligent model of analyzing clinical breast cancer data using clustering techniques with feature selection

Cited by 66 publications

References 26 publications

Analysis of k-means clustering approach on the breast cancer Wisconsin dataset

Analysis of k-means clustering approach on the breast cancer Wisconsin dataset

A fuzzy-rough nearest neighbor classifier combined with consistency-based subset evaluation and instance selection for automated diagnosis of breast cancer

RB-Bayes algorithm for the prediction of diabetic in Pima Indian dataset

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