Author : Data Mining, Classification Rule, Na�ve Bayes Algorithm, Cancer Type and Sub Type Diagnosis. 1
Date of Publication :7th May 2016
Abstract: The paper “Cancer Diagnosis Using Naive Bayes Algorithm” deals with the diagnosis of cancer using Naïve Bayes Algorithm using Gene Data Set values of previous patients. The genes expression values will be extracted using DNA micro arrays. Gene data from the cancer patients will be stored in the storage server and for the new patient; we do the necessary tests and will get the genes expression values, based on these values system will categorize the type of the cancer. Data mining technology helps in classifying cancer patients and this technique helps to identify potential cancer patients by simply analyzing the data. The need is to automate this process to make the cancer diagnosis efficient and fast with the use of state of the art technology. In recent times Computer Science has been extensively used in the field of medicine. The use of Neutral Networks and Artificial Intelligence can be seen in the diagnosis and prognosis of Cancer. Digital Image processing is used for the diagnosis of Cancer when the images of cancer cells are available. The concepts of Data Mining are used in the diagnosis of different type of diseases. Since Data Mining is mainly based on obtaining results using previously collected values, mining huge amount of data gives accurate results.
Reference :
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- Using Data Mining Techniques for Diagnosis and Prognosis of Cancer Disease by Shweta Kharya.
- Cancer Diagnosis Using Data Mining Technology by Muhammad Shahbaz, Shoaib Faruq, Muhammad Shaheen, Syed Ather Masood.
- Diagnosis of Lung Cancer Prediction System Using Data Mining Classification Techniques by V.Krishnaiah, Dr.G.Narsimha, Dr.N.Subhash Chandra.
- Early Detection and Prediction Of Lung Cancer Survival Using Neural Network Classifier by Ada, Rajneet Kaur.
- Breast Cancer Diagnosis Using Microwave And Hybrid Imaging Methods by Younis M. Abbosh
- Application of Neural Networks in Diagnosing Cancer Disease Using Demographic Data by N. Ganesan, K. Venkatesh, M. A. Rama, A. Malathi Palani
- Dr. William H. Wolberg, General Surgery Dept. University of Wisconsin, Clinical Sciences Center Madison, WI 53792. wolberg '@' eagle.surgery.wisc.edu
- W. Nick Street, Computer Sciences Dept. University of Wisconsin 1210 West Dayton St., Madison, WI 53706. street '@' cs.wisc.edu 608-262-6619
- Olvi L. Mangasarian, Computer Sciences Dept., University of Wisconsin 1210 West Dayton St., Madison, WI 53706. olvi '@' cs.wisc.edu