Weighted Naive Bayes Classifier: A Predictive Model for Breast Cancer Detection
Shweta Kharya,Sunita Soni +1 more
TLDR
Investigation of the performance criterion of a machine learning tool, Naive Bayes Classifier with a new weighted approach in classifying breast cancer is done, and experiments show that a weighted naive bayes approach outperforms naive Bayes.Abstract:
this paper investigation of the performance criterion of a machine learning tool, Naive Bayes Classifier with a new weighted approach in classifying breast cancer is done . Naive Bayes is one of the most effective classification algorithms. In many decision making system, ranking performance is an interesting and desirable concept than just classification. So to extend traditional Naive Bayes, and to improve its performance, weighted concept is incorporated. Exploration of Domain knowledge based weight assignment on UCI machine learning repository dataset of breast cancer is performed. As Breast cancer is considered to be second leading cause of death in women today. The experiments show that a weighted naive bayes approach outperforms naive bayes. KeywordsMining, Breast cancer, Naive bayes classifier, Domain based weight, Weights, Posterior probability, UCI machine learning repository, Prediction.read more
Citations
More filters
Journal ArticleDOI
Breast cancer detection by leveraging Machine Learning
TL;DR: A novel method to detect breast cancer by employing techniques of Machine Learning is presented, which has produced highly accurate and efficient results when compared to the existing methods.
Journal ArticleDOI
Breast Cancer Prediction using varying Parameters of Machine Learning Models
Puja Gupta,Shruti Garg +1 more
TL;DR: Six supervised machine learning algorithms such as k-Nearest Neighborhood, Logistic Regression, Decision Tree, Random Forest, Support Vector Machine with radial basis function kernel, and Adam Gradient Descent Learning are presented.
Journal ArticleDOI
An e-Health care services framework for the detection and classification of breast cancer in breast cytology images as an IoMT application
TL;DR: A framework which combines machine learning and computational intelligence-based approaches in e-Health care service as an application of the Internet of Medical Things (IoMT) technology, for the early detection and classification of malignant cells in breast cancer.
Journal ArticleDOI
Cloud-based decision support system for the detection and classification of malignant cells in breast cancer using breast cytology images.
Tanzila Saba,Sana Ullah Khan,Naveed Islam,Naveed Abbas,Amjad Rehman,Nadeem Javaid,Adeel Anjum +6 more
TL;DR: Experimental results show that the proposed approach has significantly improved the detection and classification of the malignant cells in breast cytology images.
Journal ArticleDOI
Feature extraction by PCA and diagnosis of breast tumors using SVM with DE-based parameter tuning
Luanyi Y. Yang,Zeshui Xu +1 more
TL;DR: A support vector machine (SVM) based diagnosing system which mainly consists of three stages and presents a superior performance when tested on the Wisconsin Diagnostic Breast Cancer data set from the University of California with fivefold cross validation.
References
More filters
Proceedings ArticleDOI
Predicting breast cancer survivability using data mining techniques
TL;DR: In this paper, appropriate and efficient networks for breast cancer knowledge discovery from clinically collected data sets are investigated and principal component techniques are used to reduce the dimension of data and find appropriate networks.
Book ChapterDOI
Methods and Problems in Data Mining
TL;DR: This work considers some methods used in data mining, concentrating on levelwise search for all frequently occurring patterns, and shows how this technique can be used in various applications.
Predicting breast cancer survivability using data mining techniques
B Abdelghani,Erhan Guven +1 more
TL;DR: This paper investigated three data mining techniques: the Naive Bayes, the back-propagated neural network, and the C4.5 decision tree algorithms, and found out that C 4.5 algorithm has a much better performance than the other two techniques.
Proceedings ArticleDOI
Learning weighted naive Bayes with accurate ranking
Harry Zhang,Shengli Sheng +1 more
TL;DR: The experiments show that a weighted naive Bayes trained to produce accurate ranking outperforms naive Baye, and various methods are explored: the gain ratio method, the hill climbing method, and the Markov chain Monte Carlo method combined with the gain Ratio method.
Book ChapterDOI
Comparison of different classification techniques using WEKA for breast cancer
TL;DR: The aim of this paper is toigate the performance of different classification or clustering methods for a set of large data and find the classification technique that has the potential to significantly improve the common or conventional methods for use in large scale data, bioinformatics or other applications.