• Corpus ID: 212463421

Mining Big Data: Breast Cancer Prediction using DT-SVM Hybrid Model

@inproceedings{Sivakami2015MiningBD,
  title={Mining Big Data: Breast Cancer Prediction using DT-SVM Hybrid Model},
  author={K. Sivakami},
  year={2015}
}
Breast Cancer is becoming a leading cause of death among women in the whole world; meanwhile, it is confirmed that the early detection and accurate diagnosis of this disease can ensure a long survival of the patients. This paper work presents a disease status prediction employing a hybrid methodology to forecast the changes and its consequence that is crucial for lethal infections. To alarm the severity of the diseases, our strategy consists of two main parts: 1. Information Treatment and… 
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References

SHOWING 1-10 OF 28 REFERENCES
An Efficient Prediction of Breast Cancer Data using Data Mining Techniques
TLDR
A comparison among the different Data mining classifiers on the database of breast cancer Wisconsin Breast Cancer, by using classification accuracy shows that Support Vector Machine (SVM) has higher prediction accuracy than those methods.
Predicting Metastasis in Breast Cancer: Comparing a Decision Tree with Domain Experts
TLDR
In situations where experienced oncologists are not available, predictive models created with data mining techniques can be used to support physicians in decision making with acceptable accuracy.
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.
DIAGNOSIS AND PROGNOSIS BREAST CANCER USING CLASSIFICATION RULES
TLDR
The purpose of this research is to develop a novel prototype of clinical problem regarding to diagnose and manage patients with breast cancer using primary dataset of breast cancer carried out from UCI dataset repository for the purpose of experimental work.
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.
An intelligent system for automated breast cancer diagnosis and prognosis using SVM based classifiers
TLDR
This paper proposes a Support Vector Machines based classifier in comparison with Bayesian classifiers and Artificial Neural Networks for the prognosis and diagnosis of breast cancer disease and provides the implementation details along with the corresponding results.
A Novel SVM and Its Application to Breast Cancer Diagnosis
TLDR
A novel method of improving the performance of a support vector machine (SVM) classifier by modifying kernel function based on the differential approximation of metric, which shows remarkable improvement of generalization error and computational cost.
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