https://github.com/Shilpi75/Breast-Cancer-Prediction

Breast Cancer Prediction using fuzzy clustering and classification
https://github.com/Shilpi75/Breast-Cancer-Prediction

breast-cancer-prediction machine-learning

Last synced: 2 months ago
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Breast Cancer Prediction using fuzzy clustering and classification

• Host: GitHub
• URL: https://github.com/Shilpi75/Breast-Cancer-Prediction
• Owner: Shilpi75
• Created: 2016-12-04T14:07:05.000Z (about 8 years ago)
• Default Branch: master
• Last Pushed: 2019-03-26T11:39:52.000Z (almost 6 years ago)
• Last Synced: 2024-08-04T10:01:25.779Z (6 months ago)
• Topics: breast-cancer-prediction, machine-learning
• Language: R
• Size: 8.79 KB
• Stars: 9
• Watchers: 4
• Forks: 3
• Open Issues: 1
• Metadata Files:
  • Readme: README.md

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• awesome-ai-cancer - Shilpi75/Breast-Cancer-Prediction - Breast Cancer Prediction using fuzzy clustering and classification (Code / Repositories)

README

        
# Breast-Cancer-Prediction

Breast Cancer Prediction using fuzzy clustering and classification

# Objective

The objective of these predictions is to assign patients to either a benign group that is noncancerous or a malignant group that is cancerous.

# Dataset

The experimental study is based on the Wisconsin Breast Cancer database from the UC Irvine Machine Learning Repository. [Dataset Link](https://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+(original))

The Breast Cancer database was obtained from the University of Wisconsin Hospitals, Madison from Dr. William H. Wolberg. It contains 699 instances, 458 (65.5%) benign and 241 (34.5%) malignant cases. Each case is characterized by 9 attributes as described by Table I and two classes (benign and malignant).

Attributes and domains are as follows: 


1. Clump Thickness:  1 – 10 


2. Uniformity of Cell Size:  1 – 10 


3. Uniformity of Cell shape:  1 – 10


4. Marginal Adhesion:  1 – 10


5. Single Epithelial Cell Size:  1 – 10


6. Bare Nuclei:  1 – 10


7. Bland Chromatin:  1 – 10


8. Normal Nucleoli:  1 – 10


9. Mitoses: 1 – 10


# Accuracy Comparison of various models

C4.5 Classifier: 89.6%


KNN Classifier: 95.4%


K means Clustering and C4.5 decision tree classifier: 95.1%


Fuzzy K means clustering and C4.5 decision tree classifier: 96.5%


K means Clustering and Fuzzy knn classifier: 93.7%


Fuzzy K means Clustering and Fuzzy knn  Classifier: 93.7%


Fuzzy K means Clustering and Fuzzy knn Classifier with feature selection (Final Model) : 96.5%