Breast Cancer Prediction Using Artificial Neural Network (ANN)

This project implements an Artificial Neural Network (ANN) model to predict breast cancer using the Breast Cancer Dataset from sklearn.datasets. The primary focus is on applying feature selection, hyperparameter tuning using grid search, and building an accurate prediction model.

Project Overview

Breast cancer is one of the most common cancers worldwide, and early detection is crucial for effective treatment. This project leverages machine learning techniques to build a predictive model that identifies the likelihood of breast cancer based on diagnostic features.

Features of the Project

• Dataset: The Breast Cancer Dataset from the sklearn.datasets library.
• Feature Selection: To identify and use the most significant features for prediction.
• Model Architecture: Artificial Neural Network (ANN) implemented using Python.
• Hyperparameter Tuning: Grid search is used to optimize model parameters.
• Development Environment: Python with VS Code as the IDE.

Dataset Details

• Source: sklearn.datasets
• Features: 30 numeric features describing the cell nucleus.
• Classes: Binary classification - Malignant (1) or Benign (0).
• Shape: 569 samples and 30 features.

Key Features in the Dataset

• Mean Radius
• Mean Texture
• Mean Perimeter
• Mean Area
• ... (and 26 other diagnostic features)

Target

• 0: Benign
• 1: Malignant

Tools and Libraries Used

• Programming Language: Python
• Libraries:
  • numpy
  • pandas
  • sklearn
  • tensorflow/keras
  • matplotlib
  • seaborn

Model Workflow

1. Data Preprocessing:

   • Load and explore the dataset.
   • Perform feature scaling and normalization.
   • Apply feature selection techniques to select the most significant features.

2. Model Building:

   • Construct an ANN architecture.
   • Train the model on the training data.

3. Hyperparameter Tuning:

   • Use Grid Search to optimize hyperparameters (e.g., learning rate, number of neurons, and activation functions).

4. Evaluation:

   • Assess the model's performance using metrics like accuracy, precision, recall, and F1-score.

How to Run the Project

1. Clone the repository:

   git clone <repository_url>
   cd breast_cancer_prediction