Breast Cancer Classification
27 May 2023
Breast Cancer Classification
Python can be a useful tool for analyzing and diagnosing breast cancer using machine learning techniques. Here’s a general outline of how we could approach it:
1. Data collection: Gather a dataset of breast cancer cases, including features such as age, tumor size, tumor type, etc., and corresponding labels indicating whether the cases are malignant or benign.
2. Data preprocessing: Clean and preprocess the dataset by handling missing values, normalizing or standardizing features, and splitting it into training and testing sets.
3. Feature selection: Identify the most relevant features that contribute to breast cancer diagnosis. This step helps reduce complexity and improve model performance.
4. Model training: Choose a suitable machine learning algorithm, such as logistic regression, support vector machines, or random forests. Train the model using the training set and evaluate its performance.
5. Model evaluation: Assess the performance of the trained model using evaluation metrics like accuracy, precision, recall, and F1 score. Adjust hyperparameters if necessary to improve the model’s performance.
6. Prediction and diagnosis: Apply the trained model to new, unseen data to predict whether a given case is malignant or benign. This can assist in diagnosing breast cancer based on the available features.
Python provides powerful libraries like scikit-learn, pandas, and numpy that can facilitate these steps.
I use these libraries for classification of a dataset of breast cancer:
Keras, Sklearn, pandas, TensorFlow, Numpy and so on.
- About this project
In March 2023, I developed a machine learning project to classify a ” breast cancer” dataset.
As I mentioned, I utilized various Python libraries such as TensorFlow, Sklearn, Keras, and more.
How I deploy this method?
For Data preprocessing, I imported the breast cancer dataset from “sklearn datasets” . The shape of this dataset was (569,30).
For Modeling, the “sigmoid” used for activator, and “adam” for optimizer. The “accuracy“ used for metrices.
The results of this project were impressive, achieving an accuracy 97% and a loss of 0.07.
I shown plots of the “accuracy” and “loss” above.
Posted in Python