https://github.com/dark-data/breast-cancer-prediction

Over the past few decades, ML techniques have been widely used in intelligent healthcare systems, especially for breast cancer (BC) diagnosis and prognosis. Traditionally the diagnostic accuracy of a patient depends on a physician’s experience. however, this expertise is built up over many years of observations of different patient’s symptoms and confirmed diagnoses. ML techniques can take over some complex manual works from the physicians. Recently, ML techniques are playing a significant role in diagnosis of BC by applying classification techniques to identify people with BC, distinguish benign from malignant tumours and to predict weather the patient is affected or not. We focus on the neural network (NN), support vector machine (SVMs) and k-nearest neighbor (k-NNs) techniques in BC diagnosis.
https://github.com/dark-data/breast-cancer-prediction

breast-cancer-wisconsin ipynb-jupyter-notebook knn-classifier neural-networks svm-classifier

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Over the past few decades, ML techniques have been widely used in intelligent healthcare systems, especially for breast cancer (BC) diagnosis and prognosis. Traditionally the diagnostic accuracy of a patient depends on a physician’s experience. however, this expertise is built up over many years of observations of different patient’s symptoms and confirmed diagnoses. ML techniques can take over some complex manual works from the physicians. Recently, ML techniques are playing a significant role in diagnosis of BC by applying classification techniques to identify people with BC, distinguish benign from malignant tumours and to predict weather the patient is affected or not. We focus on the neural network (NN), support vector machine (SVMs) and k-nearest neighbor (k-NNs) techniques in BC diagnosis.

• Host: GitHub
• URL: https://github.com/dark-data/breast-cancer-prediction
• Owner: dark-data
• Created: 2020-07-09T13:47:37.000Z (about 4 years ago)
• Default Branch: master
• Last Pushed: 2020-07-09T13:57:24.000Z (about 4 years ago)
• Last Synced: 2024-09-23T22:02:37.266Z (2 days ago)
• Topics: breast-cancer-wisconsin, ipynb-jupyter-notebook, knn-classifier, neural-networks, svm-classifier
• Language: Jupyter Notebook
• Homepage:
• Size: 151 KB
• Stars: 2
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Breast-cancer-prediction

Over the past few decades, ML techniques have been widely used in intelligent healthcare systems, especially for breast cancer (BC) diagnosis and prognosis. Traditionally the diagnostic accuracy of a patient depends on a physician’s experience. however, this expertise is built up over many years of observations of different patient’s symptoms and confirmed diagnoses. ML techniques can take over some complex manual works from the physicians. Recently, ML techniques are playing a significant role in diagnosis of BC by applying classification techniques to identify people with BC, distinguish benign from malignant tumours and to predict weather the patient is affected or not. We focus on the neural network (NN), support vector machine (SVMs) and k-nearest neighbor (k-NNs) techniques in BC diagnosis.