A woman who has had breast cancer in one breast is at an increased risk of developing cancer in her other breast. Each one is associated with different prognoses and treatments and therapies. Research shows that experienced physicians can detect cancer by 79% accuracy, while a 91 % (sometimes up to 97%) accuracy can be achieved using Machine Learning techniques.
Explore and run machine learning code with Kaggle Notebooks | Using data from Breast Cancer Wisconsin (Diagnostic) Data Set Having other relatives with breast cancer may also raise the risk. In this tutorial, you will learn how to train a Keras deep learning model to predict breast cancer in breast histology images. This dataset holds 2,77,524 patches of size 50×50 extracted from 162 whole mount slide images of breast cancer specimens scanned at 40x. Explore and run machine learning code with Kaggle Notebooks | Using data from Breast Cancer Wisconsin (Diagnostic) Data Set A woman has a higher risk of breast cancer if her mother, sister or daughter had breast cancer, especially at a young age (before 40). Output : RangeIndex: 569 entries, 0 to 568 Data columns (total 33 columns): id 569 non-null int64 diagnosis 569 non-null object radius_mean 569 non-null float64 texture_mean 569 non-null float64 perimeter_mean 569 non-null float64 area_mean 569 non-null float64 smoothness_mean 569 non-null float64 compactness_mean 569 non-null float64 concavity_mean 569 non-null float64 concave …
We’ll use the IDC_regular dataset (the breast cancer histology image dataset) from Kaggle. Of these, 1,98,738 test negative and 78,786 test positive with IDC. Genetic factors. Breast cancer is generally classified into five molecular subtypes: Luminal A, Luminal B, HER2, Basal like and Normal. Machine Learning technique can dramatically improve the level of diagnosis in breast cancer. Family history of breast cancer. Technical advice from other data scientists | Questions & Answers