eskayML / breast_cancer

Breast Cancer Detection App

The breast cancer detection app is a machine learning application that utilizes Scikit-learn to train a predictive model. The model is trained on a dataset of tabular data containing various features associated with breast cancer. The features are used to train the model to predict whether a given case is benign or malignant .The machine application is designed to be user-friendly and accessible to healthcare professionals, allowing them to quickly and accurately diagnose breast cancer using this powerful predictive tool. It has a performance of 97% accuracy on a hold-out test set.

Data Collection

The WISCONSIN Breast Cancer dataset is a widely used benchmark dataset for binary classification tasks. It contains information about breast cancer tumors, including attributes such as size, shape, and texture. There are a total of 569 instances in the dataset, with 212 malignant (M) and 357 benign (B) cases

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Target Variable

The target contains 212 Malignant and 357 benign samples.

Features

Here contains all the features that were used in training the model

radius_mean

The mean distance from the center to points on the perimeter of the tumor.

perimeter_mean

The perimeter (total length of the boundary) of the tumor.

area_mean

The area enclosed by the perimeter of the tumor.

concavity_mean

A measure of the severity of concave portions of the contour of the tumor.

concave points_mean

The number of concave portions of the contour of the tumor.

radius_worst

The largest distance from the center to points on the perimeter of the tumor.

perimeter_worst

The largest perimeter (total length of the boundary) of the tumor.

area_worst

The largest area enclosed by the perimeter of the tumor.

concavity_worst

The largest measure of the severity of concave portions of the contour of the tumor.

concave points_worst

The largest number of concave portions of the contour of the tumor.

Training / Testing

The model was trained on 80% of the dataset, and was evaluated on the remaining 20% of the data.

Evaluation

The classification report shows the precision and recall scores on each of the labels and also the f1-scores.

The confusion matrix shows the distribution of predictions and how well they got predicted. On the test set , we have no false positives and very little false negatives

Testing the Application

You can test the flask application using pytest by running

pytest

in the root directory of the project

Dependencies

• Python >= 3.7
• Flask >= 2.2.1
• Scikit-learn >= 1.2.1