A Breast Cancer Diagnosis Method based on VIM Feature Selection and Hierarchical Clustering Random Forest Algorithm
Product Description
Aim:
To detect the breast cancer using K Nearest Neighbor algorithms with the help of data mining techniques.
Abstract:
Breast cancer is one of the most common cancers among the women. Breast cancer can be classified into two benign and malignant. The etiological reason behind the cancer is due to the generation of superoxide’s or free radicals. X-Ray, MRI, Biopsy test are used to detect diseases in now a days. It takes long time for diagnosis .In our study Data mining techniques of machine learning are used to detect disease.
Proposed system:
In Proposed system, Data mining is a field of study within machine learning and focuses on research data analysis through unauthorized learning. According to data provided by World health organization (WHO) two million new cases are reported and which of 626,679 were died in year 2018. At this situation the importance of machine learning can be realized. For the early detection of disease machine learning is very helpful. Here for mostly data collected is made to feature selection and classification the separation is done by KNN algorithm. User may give the input in the way of Web application.
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The Delivery time for Hardware
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Mini Projects: Software Includes
- Demo Video
- Abstract
- Base paper
- Full Project PPT
- UML Diagrams
- SRS
- Source Code
- Screen Shots
- Software Links
- Reference Papers
- Full Project Documentation
- Online support
The
Delivery time for software Miniprojects is 2 -3 working days.
Mini Projects - Hardware includes
- Demo Video
- Abstract
- PPT
- Datasheets
- Circuit Diagrams
- Source Code
- Screen Shots & Photos
- Software Links
- Reference Papers
- Full Project Documentation
- Online
support
The Delivery time for Hardware Mini projects is 7-8 working days.