Breast Cancer Classification

ML model for breast cancer diagnosis with 96.5% accuracy

Overview

Developed a machine learning model to classify breast cancer patients into malignant and benign categories, achieving high accuracy in early detection.

Technical Details

  • Technologies: Python, scikit-learn, Keras
  • Best Model: Random Forest Classifier
  • Accuracy: 96.50%
  • Recall: 95.47%

Approach

  1. Data Preprocessing: Handled missing values and normalized features
  2. Feature Engineering: Selected most relevant diagnostic features
  3. Model Selection: Compared multiple classifiers (SVM, Random Forest, Neural Networks)
  4. Evaluation: Used cross-validation and confusion matrix analysis

Results

The Random Forest classifier achieved the best performance with:

  • High accuracy (96.50%) for reliable predictions
  • Strong recall (95.47%) to minimize false negatives, crucial for cancer detection

Impact

This project demonstrates the potential of ML in healthcare for early cancer detection, potentially improving patient outcomes through timely diagnosis.