Breast Cancer Classification

This document outlines a case study for breast cancer classification using machine learning. The goal is to develop a model that supports early diagnosis by differentiating between malignant and benign tumors based on extracted features from diagnostic data.

Project Overview

• Objective:
  To assist medical diagnosis by providing a machine learning model that classifies tumor images as either malignant or benign, thereby increasing early detection rates and improving patient outcomes.

• Key Highlights:

  • Early diagnosis of breast cancer significantly improves survival.
  • Utilization of machine learning allows for automated feature extraction and classification, minimizing human intervention.
  • The approach involves processing medical images to extract relevant features and training a classifier to predict the nature of the tumor.

Dataset Details

• Total Instances: 569
• Features: 30 clinically relevant features (e.g., radius, area, smoothness, etc.)
• Target Classes:
  • 0 for benign
  • 1 for malignant
• Class Distribution:
  • 212 instances: malignant
  • 357 instances: benign

Methodology

1. Data Acquisition and Preprocessing:

   • Collection: Data is obtained from diagnostic imaging and clinical records.
   • Preprocessing: The data is cleaned and normalized to ensure consistent feature scaling and quality.

2. Feature Extraction:

   • Morphological features such as tumor radius, area, and smoothness are computed.
   • These features serve as inputs to the machine learning model.

3. Model Training:

   • Classifier: A Support Vector Machine (SVM) is utilized.
   • Separation: The SVM identifies an optimal hyperplane to distinguish between the malignant and benign classes.

4. Evaluation and Testing:

   • The dataset is split into training and testing sets.
   • The model’s performance is assessed using metrics such as accuracy, precision, recall, and F1-score.

Conclusion

This project demonstrates the potential of machine learning in supporting early diagnosis of breast cancer. By automating the classification process using an SVM-based approach, the model contributes to efficient and reliable decision-making in clinical environments.

For more detailed instructions on data processing and model implementation, please refer to the additional project files.