Breast cancer prediction involves utilizing data analysis, machine learning, and risk assessment models to estimate an individual's likelihood of developing breast cancer, facilitating proactive measures for early detection and intervention.
The goal of breast cancer prediction in machine learning is to develop accurate and efficient algorithms that analyze diverse data, including genetic information and clinical parameters, to predict the likelihood of an individual developing breast cancer, enabling timely intervention and personalized healthcare.
- ID number
- 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 ("coastline approximation" - 1)
The mean, standard error and "worst" or largest (mean of the three largest values) of these features were computed for each image, resulting in 30 features. For instance, field 3 is Mean Radius, field 13 is Radius SE, field 23 is Worst Radius.
All feature values are recoded with four significant digits.
Missing attribute values: none
Class distribution: 357 benign, 212 malignant
- pandas
- numpy
- matplotlib
- seaborn
- sklearn
- flask
- HTML
- CSS
- Bootstrap
https://github.com/shailesh2210/Kidney-disease-prediction.git
conda create -n venv python=3.8 -yconda activate venvpip install -r requirements.txtpython app.py