• Corpus ID: 13171440

DATA MINING CLASSIFICATION TECHNIQUES APPLIED FOR BREAST CANCER DIAGNOSIS AND PROGNOSIS

@inproceedings{Gupta2011DATAMC,
  title={DATA MINING CLASSIFICATION TECHNIQUES APPLIED FOR BREAST CANCER DIAGNOSIS AND PROGNOSIS},
  author={Shelly Gupta and Dharminder Kumar and Anand Kumar Sharma},
  year={2011}
}
Breast Cancer Diagnosis and Prognosis are two medical applications pose a great challenge to the researchers. The use of machine learning and data mining techniques has revolutionized the whole process of breast cancer Diagnosis and Prognosis. Breast Cancer Diagnosis distinguishes benign from malignant breast lumps and Breast Cancer Prognosis predicts when Breast Cancer is likely to recur in patients that have had their cancers excised. Thus, these two problems are mainly in the scope of the… 

Figures from this paper

Using data mining techniques for diagnosis and prognosis of cancer disease
TLDR
This study paper summarizes various review and technical articles on breast cancer diagnosis and prognosis and focuses on current research being carried out using the data mining techniques to enhance the breast cancer diagnosed and prog outlook.
Breast Cancer Classification using Decision Tree Algorithms
TLDR
A decision tree-based data mining technique for breast cancer early detection with highest accuracy, which helps patients to recover and justifies the use of the proposed machine learning-based Decision Tree classifier in pre-evaluating patients for triage and decision-making prior to the availability of data.
Constructing a Predictive Model for Detection of Breast Cancer
TLDR
A predictive model using data mining techniques to identify hidden knowledge and develop a prototype interface for breast cancer that support health professional in their diagnosis decisions and treatment planning measures are constructed.
Breast Cancer Diagnosis and Prediction Using Machine Learning and Data Mining Techniques : A Review
TLDR
The role of machine learning and data mining techniques in breast cancer detection and diagnosis is reviewed and different classification algorithms to breast cancer prediction such as Decision tree, Naïve Bayes, and Artificial Neural Network are compared.
A Review on Data Mining Techniques for Prediction of Breast Cancer Recurrence
TLDR
Data mining algorithms provide assistance in predicting the early-stage breast cancer that continually has been difficult analysis drawback and can establish the most effective algorithm that predicts the recurrence of the breast cancer and improve the accuracy the algorithms.
Breast Cancer Prediction and Detection Using Data Mining Classification Algorithms: A Comparative Study
  • Kaya Keleş
  • Computer Science
    Tehnicki vjesnik - Technical Gazette
  • 2019
TLDR
It can be said that if a patient has a breast cancer tumor, detection of the tumor is possible, and several classification algorithms for breast cancer diagnosis using a data set from the measurements of an antenna with a 10-fold cross-validation method were compared.
Diagnosis of Breast Cancer Using Ensemble of Data Mining Classification Methods
TLDR
The results show that the ensemble model is better than the individual models according to the evaluation metric which is the accuracy and feature selection technique is applied to increase the efficiency of the models.
Dimensionality Reduction and Comparison of Classification Models for Breast Cancer Prognosis
TLDR
A comparative model has been developed that compare performance of various data mining technique on the dataset and reveals that BayesNet is the best classifier that correctly predicts cancer survivability in the patient and KStar is the fastest algorithm that takes lowest computation time for the classification.
Ensemble Learning Method for the Prediction of Breast Cancer Recurrence
TLDR
The aim of this research is to improve the prediction of breast cancer recurrence using an ensemble learning technique and to provide a website that enables physicians to enter features related to a breast cancer patient and get the probability of breastcancer recurrence.
Breast Cancer Detection using Neural Network Mammogram
TLDR
This paper includes uses of various Data Mining along with neural networks to identify the presence of breast cancer at early stages and diagnose it efficiently.
...
...

References

SHOWING 1-10 OF 31 REFERENCES
Predicting breast cancer survivability using data mining techniques
TLDR
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.
Artificial Neural Networks Applied to Survival Prediction in Breast Cancer
TLDR
An artificial neural network is very accurate in the 5-, 10- and 15-year breast-cancer-specific survival prediction and the good predictive performance of a network trained without information on nodal status demonstrate that neural networks can be important tools for cancer survival prediction.
A Hybrid Bayesian Network Model for Predicting Breast Cancer Prognosis
TLDR
The proposed hybrid BN model for breast cancer prognosis predictin may be useful for clinicians in the medical fields, as the model provides both high degree of performance inherited from ANN and good explanation power from BN.
Predicting breast cancer survivability using fuzzy decision trees for personalized healthcare.
TLDR
Performance comparisons suggest that, for cancer prognosis, hybrid fuzzy decision tree classification is more robust and balanced than independently applied crisp classification; moreover it has a potential to adapt for significant performance enhancement.
Using data mining for assessing diagnosis of breast cancer
TLDR
SVM technique shows promising results for increasing diagnostic accuracy of classifying the cases witnessed by the largest area under the ROC curve, comparable to empirical ROC and binomial ROC of 0.85323 and 0.57575.
PREDICTING BREAST CANCER SURVIVABILITY USING DATA MINING TECHNIQUES
TLDR
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.
A Data Mining Approach for the Detection of High-Risk Breast Cancer Groups
TLDR
It is shown that it is possible to find statistically significant associations with breast cancer by deriving a decision tree and selecting the best leaf, and permutation tests were used.
Classification Rule Construction Using Particle Swarm Optimization Algorithm for Breast Cancer Data Sets
TLDR
This proposed work is intended to develop Classification rules to extract data from historical or training data of patients which is developed into patterns relevant for diagnosis and suitable for quicker analysis, automated processing, thus reducing cost and helping to provide enhanced care and better cure.
Neural Network Aided Breast Cancer Detection and Diagnosis Using Support Vector Machine
TLDR
The proposed neural network model hold promise for radiologists, surgeons, and patients with information, which was previously available only through biopsy, thus substantially reducing the number of unnecessary surgical procedures.
...
...