Maximizing-Performance-in-Breast-Cancer-PrMaximizing Performance in Breast Cancer Prediction through Machine Learning, Hyperparameter Tuning, and PCA Analysis
Breast cancer is one of the most common types of cancer in women, and early diagnosis is crucial for improving the chances of successful treatment. Machine learning techniques have been widely used to develop predictive models for breast cancer diagnosis and prognosis, and understanding the structure of the data is an important step in building these models.
In this article, we will explore the structure of breast cancer data and discuss some techniques for visualizing and summarizing the data. We will use the Breast Cancer Wisconsin (Diagnostic) dataset from the UCI Machine Learning Repository as an example. This dataset contains 569 samples of biopsy tissue collected from breast masses and contains 30 features for each sample. The goal is to predict whether the tissue is benign (non-cancerous) or malignant (cancerous) based on the features.
Introduction Dataset Description The Breast Cancer Wisconsin (Diagnostic) DataSet, obtained from Kaggle, contains features computed from a digitized image of a fine needle aspirate (FNA) of a breast mass and describe characteristics of the cell nuclei present in the image.
Number of instances: 569
Number of attributes: 32 (ID, diagnosis, 30 real-valued input features)
Attribute information
- ID number
- Diagnosis (M = malignant, B = benign)
- Ten real-valued features are computed for each cell nucleus:
- radius (mean of distances from center to points on the perimeter)
- texture (standard deviation of gray-scale values)
- perimeter
- area
- smoothness (local variation in radius lengths)
- compactness (perimeter^2 / area - 1.0)
- concavity (severity of concave portions of the contour)
- concave points (number of concave portions of the contour)
- symmetry
- 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.ediction