Breast Cancer Prediction using Simple Multilayer Perceptron Neural Network (Feed Forward Model)

Overview

This project involves predicting breast cancer using a simple multilayer perceptron neural network (feed-forward model). The dataset used for this project is the Breast Cancer Wisconsin (Diagnostic) Dataset, which is a well-known dataset for breast cancer classification.

Developer Information

Developer: Yuvraj Singh Chowdhary

Contact: LinkedIn Profile

Project Report

You can view the detailed project report here.

Table of Contents

• Overview
• Dataset
• Installation
• Usage
• Results
• Contributing
• License

Dataset

The Breast Cancer Wisconsin (Diagnostic) Dataset is used for this project. It contains 569 samples of malignant and benign tumor cells. The dataset features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. They describe the characteristics of the cell nuclei present in the image.

• Features:

  • Mean radius
  • Mean texture
  • Mean perimeter
  • Mean area
  • Mean smoothness
  • Mean compactness
  • Mean concavity
  • Mean concave points
  • Mean symmetry
  • Mean fractal dimension
  • etc.

• Target:

  • 0: Malignant

  • 1: Benign

  • 

Installation

To run this project, you will need Python and several libraries. You can install the necessary libraries using the following commands:

pip install numpy pandas matplotlib scikit-learn seaborn

Usage

1. Import Libraries

   Import the necessary libraries for data manipulation, visualization, and machine learning.

   import numpy as np
   import pandas as pd
   import matplotlib.pyplot as plt
   import sklearn.datasets
   from sklearn.model_selection import train_test_split
   import seaborn as sns

2. Load Dataset

   Load the breast cancer dataset from sklearn.

   breast_cancer_dataset = sklearn.datasets.load_breast_cancer()

3. Create DataFrame

   Create a DataFrame from the dataset and add the target column.

   data_frame = pd.DataFrame(breast_cancer_dataset.data, columns = breast_cancer_dataset.feature_names)
   data_frame['label'] = breast_cancer_dataset.target

4. Data Preprocessing

   • Check for null values
   • Display statistical measures
   • Check distribution of target variable

   data_frame.isnull().sum()
   data_frame.describe()
   data_frame['label'].value_counts()

5. Split Data

   Split the data into features (X) and target (Y), then into training and test sets.

   X = data_frame.drop(columns='label', axis=1)
   Y = data_frame['label']
   X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=2)

6. Feature Scaling

   Scale the features using StandardScaler.

   from sklearn.preprocessing import StandardScaler
   scaler = StandardScaler()
   X_train_std = scaler.fit_transform(X_train)
   X_test_std = scaler.transform(X_test)

7. Build and Train Model

   Build and train a multilayer perceptron neural network model.

   model = keras.Sequential([
                         keras.layers.Flatten(input_shape=(30,)),
                         keras.layers.Dense(20, activation='relu'),
                         keras.layers.Dense(2, activation='sigmoid')
   ])
   model.compile(optimizer='adam',
             loss='sparse_categorical_crossentropy',
             metrics=['accuracy'])
   history = model.fit(X_train_std, Y_train, validation_split=0.1, epochs=10)

   

8. Evaluate Model

   Evaluate the performance of the model.

   loss, accuracy = model.evaluate(X_test_std, Y_test)
   print(accuracy,loss)

Results

• After training the Multi Perceptron Model with 10 epochs, we achieved an accuracy of 95% on the test dataset.

Contributing

If you want to contribute to this project, please fork the repository and create a pull request. For major changes, please open an issue first to discuss what you would like to change.

License

This project is licensed under the MIT License. See the LICENSE file for more details.

---