Earticle

초록

영어

In Recent years the way we analyze the breast cancer has changed dramatically. Breast cancer is the most common and complex disease diagnosed among women. There are several subtypes of breast cancer and many options are there for the treatment. The most important is to educate the patients. As the research continues to expand, the understanding of the disease and its current treatments types, the researchers are constantly being updated with new researching techniques. Breast cancer survival rates have been increased with the use of new advanced treatments, largely due to the factors such as earlier detection, a new personalized approach to treatment and a better understanding of the disease. Many machine learning classification models have been adopted and modified to diagnose the breast cancer disease. In order to enhance the performance of classification model, our research proposes a model using A Hybrid Modified K-Means Clustering with Modified SVM (Support Vector Machine) Machine learning algorithm to create a new method which can highly improve the performance and prediction. The proposed Machine Learning model is to improve the performance of machine learning classifier. The Proposed Model rectifies the irregularity in the dataset and they can create a new high quality dataset with high accuracy performance and prediction. The recognized datasets Wisconsin Diagnostic Breast Cancer (WDBC) Dataset have been used to perform our research. Using the Wisconsin Diagnostic Breast Cancer (WDBC) Dataset, We have created our Model that can help to diagnose the patients and predict the probability of the breast cancer. A few machine learning classifiers will be explored in this research and compared with our Proposed Model “A Hybrid Modified K-Means with Modified SVM Machine Learning Algorithm to Enhance the Cancer Prediction” to implement and evaluated. Our research results show that our Proposed Model has a significant performance compared to other previous research and with high accuracy level of 99% which will enhance the Cancer Prediction.

목차

Abstract
1. Introduction
2. Related Research
3. Proposed Model
3.1 Preprocess and Visualize the (WDBC) Dataset.
4. Experimental Evaluation
4.1 Experiment Environment
4.2 Dataset
4.3 Evaluation of P-Model on Standardized Data
4.4 Measure for Performance Evaluation / Experimental Results
5. Conclusion and Future Work
References

키워드

저자

• Rethina Kumar [ Assistant Professor, Dept of Information and Communication, Dong Seoul University, Korea. Research Scholar, Bharathidasan University, India. ] Corresponding Author
• Gopinath Ganapathy [ Professor, Registrar, Bharathidasan University, India. ]
• Jeong-Jin Kang [ Professor, Dept. of Information and Communication, Dong Seoul University, Seongnam Korea ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Internet, Broadcasting and Communication

• 간기

  계간

• pISSN

  2288-4920

• eISSN

  2288-4939

• 수록기간

  2009~2025

• 십진분류

  KDC 326 DDC 380