Breast cancer is the most frequent cancer among women, impacting 2.1 million women each year, and also causes the greatest number of cancer-related deaths among women. In 2018, it is estimated that 627,000 women died from breast cancer – that is approximately 15% of all cancer deaths among women (as taken from WHO). Early detection of breast cancer is possible using Artificial Intelligence and Machine Learning methods.

The dataset used to train and test the prediction model was obtained from the UCI Machine Learning Repository.

1. https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Diagnostic%29
2. https://www.kaggle.com/uciml/breast-cancer-wisconsin-data

The original dataset has also been uploaded to this repository and can be directly used.

The dataset consists of the features of a digitized image of a breast cell mass. Attributes information:

1. ID number
2. Diagnosis (M = malignant, B = benign)

Ten real-valued features are computed for each cell nucleus:

a) radius (mean of distances from center to points on the perimeter)
b) texture (standard deviation of gray-scale values)
c) perimeter
d) area
e) smoothness (local variation in radius lengths)
f) compactness (perimeter^2 / area - 1.0)
g) concavity (severity of concave portions of the contour)
h) concave points (number of concave portions of the contour)
i) symmetry
j) fractal dimension

For every sample, the mean, standard error and largest values (mean of the three largest values) were computed for each image, resulting in a total of 30 features.

The original data was cleaned after performing exploratory data analysis (EDA). The cleaned dataset can also be found for direct use in this repository. A Support Vector Classifier (SVC) was used as the prediction model, in unison with Grid Search hyperparameter tuning (with 3-fold cross validation). recall_score was used as the scoring parameter, as with medical aplications, we need to have high number of both true and false positives. It is better to have a wrong diagnosis than a missed one.

The final classifier had a recall_score of approximately 0.901. Hence, further improvements are required to make the classification even more accurate.