Naïve Bayes Model Based Improved K-Nearest Neighbor Classifier for Breast Cancer Prediction

Goyal, Sonia; Maheshwar, .

doi:10.1007/978-981-15-0108-1_1

Cited by 4 publications

(2 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Yue et al examined machine learning (ML) techniques used for the diagnosis and prognostics of breast cancer [20]. The researchers based their study on studies with the aid of SVM [21], NN [22], KNN [23], [24], and DT algorithms [25]. In addition, the breast cancer list from Wisconsin was used.…”

Section: Related Workmentioning

confidence: 99%

An Effective of Ensemble Boosting Learning Method for Breast Cancer Virtual Screening Using Neural Network Model

Osman

Aljahdali

2020

IEEE Access

View full text Add to dashboard Cite

Radial Based Function Neural Network models (RBFNN) are currently used deep-rooted methods for assessing the stages of diagnosis of chronic diseases. The goals of this research are to suggest a model for the diagnosis of breast cancer, and to be able to estimate the stages of development of premalignant breast tumors. The significance of the study is to develop an integrated RBF neural network with ensemble features using the boosting method. The importance of the ensemble boosting method is to generate a sequence of models to achieve more precise predictions. One of the ensemble boosting advantages is that it can take longer to build and to score than a RBF NN standard model. By using ensemble boosting, the accuracy of breast tumor diagnosis increased and thus it became easier to know the stage of the tumor, and whether it was malignant or benign. This will help doctors to select appropriate treatment for each tumor stage, consequently leading to the salvage of cancer patients with this type of tumor. The suggested RBFNN method was examined on the different type of UCI breast cancer datasets. The general diagnosis accuracy based on 10-fold cross validation using RBFNN method obtained 97.4%, 98.4%, 97.7% and 97.0% for the WBC, BCD, BCP, and WBCD UCI datasets respectively. The effectiveness of the proposed method was confirmed by comparing accuracy improvement both before and after using ensemble boosting, and it was found to be more accurate compared with other breast cancer diagnosis methods such as Logistic Regression (91.5%), KNN (96%), SVM (89%), Decision tree (95.13), CNN (97.66%), and Naive Bayes (91%).INDEX TERMS Cancer disease, ensemble boosting, prediction, radial based function, neural network.

show abstract

Section: Related Workmentioning

confidence: 99%

An Effective of Ensemble Boosting Learning Method for Breast Cancer Virtual Screening Using Neural Network Model

Osman

Aljahdali

2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…The KNN algorithm is considered lazy learning because it rely on predictions from just only specific selection of instances most similar to the test set instance, (Gongde, Hui, David, & Yaxin, 2004;Sonia & Maheshwar 2019). beside that it is the most simple method or algorithm for data mining and machine language, (Mohammad et al, 2016).…”

Section: K-nearest Neighbors Algorithm (Knn)mentioning

confidence: 99%

Application of Naïve Bayes, Decision Tree, and K-Nearest Neighbors for Automated Text Classification

Ababneh¹

2019

MAS

View full text Add to dashboard Cite

Nowadays, many applications that use large data have been developed due to the existence of the Internet of Things. These applications are translated into different languages and require automated text classification (ATC). The ATC process depends on the content of one or more predefined classes. However, this process is problematic for the Arabic translation of the data. This study aims to solve this issue by investigating the performances of three classification algorithms, namely, k-nearest neighbor (KNN), decision tree (DT), and naïve Bayes (NB) classifiers, on Saudi Press Agency datasets. Results showed that the NB algorithm outperformed DT and KNN algorithms in terms of precision, recall, and F1. In future works, a new algorithm that can improve the handling of the ATC problem will be developed.

show abstract

Machine Learning‐Based Virus Type Classification Using Transmission Electron Microscopy Virus Images

Jena

Bhoi

Nayak

et al. 2021

Machine Vision Inspection Systems, Volume 2

View full text Add to dashboard Cite

Naïve Bayes Model Based Improved K-Nearest Neighbor Classifier for Breast Cancer Prediction

Cited by 4 publications

References 12 publications

An Effective of Ensemble Boosting Learning Method for Breast Cancer Virtual Screening Using Neural Network Model

An Effective of Ensemble Boosting Learning Method for Breast Cancer Virtual Screening Using Neural Network Model

Application of Naïve Bayes, Decision Tree, and K-Nearest Neighbors for Automated Text Classification

Machine Learning‐Based Virus Type Classification Using Transmission Electron Microscopy Virus Images

Contact Info

Product

Resources

About