Classification of breast cancer using Wrapper and Naïve Bayes algorithms

Maysanjaya, I Made Dendi; Pradnyana, I Made Ardwi; Putrama, I Made

doi:10.1088/1742-6596/1040/1/012017

Cited by 12 publications

(3 citation statements)

References 11 publications

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“…Besides, it performs well in categorical input compared with the normalized bell curve. Several studies have been carried out using the NB algorithm on healthcare data [7][13] [15], and findings confirm that it is a good classifier in predicting healthcarerelated cases.…”

Section: Nbmentioning

confidence: 79%

Predicting breast cancer recurrence using principal component analysis as feature extraction: an unbiased comparative analysis

Zain

Alshenaifi

Aljaloud

et al. 2020

Int. J. Adv. Intell. Informatics

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Breast cancer recurrence is among the most noteworthy fears faced by women. Nevertheless, with modern innovations in data mining technology, early recurrence prediction can help relieve these fears. Although medical information is typically complicated, and simplifying searches to the most relevant input is challenging, new sophisticated data mining techniques promise accurate predictions from high-dimensional data. In this study, the performances of three established data mining algorithms: Naïve Bayes (NB), k-nearest neighbor (KNN), and fast decision tree (REPTree), adopting the feature extraction algorithm, principal component analysis (PCA), for predicting breast cancer recurrence were contrasted. The comparison was conducted between models built in the absence and presence of PCA. The results showed that KNN produced better prediction without PCA (F-measure = 72.1%), whereas the other two techniques: NB and REPTree, improved when used with PCA (F-measure = 76.1% and 72.8%, respectively). This study can benefit the healthcare industry in assisting physicians in predicting breast cancer recurrence precisely.

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Section: Nbmentioning

confidence: 79%

Predicting breast cancer recurrence using principal component analysis as feature extraction: an unbiased comparative analysis

Zain

Alshenaifi

Aljaloud

et al. 2020

Int. J. Adv. Intell. Informatics

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“…The results showed that the NB classifier provides better performances [20][21]. The accuracy, sensitivity and specificity up to 90% and above [22]. Dian E. R., Nurizal D. P. & Machsus Machsus [23] is promising and able to enhance the prediction on Breast Cancer Wisconsin data.…”

Section: Introductionmentioning

confidence: 99%

Comparative analysis on bayesian classification for breast cancer problem

et al. 2019

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The problem of imbalanced class distribution or small datasets is quite frequent in certain fields especially in medical domain. However, the classical Naive Bayes approach in dealing with uncertainties within medical datasets face with the difficulties in selecting prior distributions, whereby parameter estimation such as the maximum likelihood estimation (MLE) and maximum a posteriori (MAP) often hurt the accuracy of predictions. This paper presents the full Bayesian approach to assess the predictive distribution of all classes using three classifiers; naïve bayes (NB), bayesian networks (BN), and tree augmented naïve bayes (TAN) with three datasets; Breast cancer, breast cancer wisconsin, and breast tissue dataset. Next, the prediction accuracies of bayesian approaches are also compared with three standard machine learning algorithms from the literature; K-nearest neighbor (K-NN), support vector machine (SVM), and decision tree (DT). The results showed that the best performance was the bayesian networks (BN) algorithm with accuracy of 97.281%. The results are hoped to provide as base comparison for further research on breast cancer detection. All experiments are conducted in WEKA data mining tool.

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“…In classification, there are several methods, namely decision trees, artificial neural networks, rough set theory, fuzzy theory and logic that have their respective functions in the algorithm [8]. Decision tree is a process of classifying unknown data by performing a top-down search strategy for the solution [9].…”

Section: Introductionmentioning

confidence: 99%

Data Mining utilization in Determining Strategic Business Area Restaurants by Using C4.5 Algorithm

Christianto¹,

Wahab²

2019

Proceedings of the Proceedings of the 1st International Conference on Informatics, Engineering, Science and Technology, INCITES

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The purpose of this study is to help in analyzing the feasibility of business locations quickly. The research method used is qualitative research to describe the decision tree with C4.5 algorithm to easily solve the problem by analyzing the data mining usage. The results of this study are in the form of knowledge as a basis for decision making. In this research, data mining will be carried out so it can produce decisions in determining strategic business locations. It can be concluded that the use of Data Mining in Determining the Strategic Restaurant Business Area by Using C4.5 Algorithm can help in analyzing the feasibility of business locations quickly.

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Classification of breast cancer using Wrapper and Naïve Bayes algorithms

Cited by 12 publications

References 11 publications

Predicting breast cancer recurrence using principal component analysis as feature extraction: an unbiased comparative analysis

Predicting breast cancer recurrence using principal component analysis as feature extraction: an unbiased comparative analysis

Comparative analysis on bayesian classification for breast cancer problem

Data Mining utilization in Determining Strategic Business Area Restaurants by Using C4.5 Algorithm

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