https://github.com/nafisalawalidris/logistic-regression-model-for-breast-cancer-recurrence-prediction

Predicting Breast Cancer Recurrence - A logistic regression model using patient attributes to classify recurrence risk. Dataset analysis and model evaluation. Contributions welcome.
https://github.com/nafisalawalidris/logistic-regression-model-for-breast-cancer-recurrence-prediction

breast-cancer classification-model data-analysis data-science healthcare logistic-regression machine-learning python recurrence-prediction scikit-learn

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Predicting Breast Cancer Recurrence - A logistic regression model using patient attributes to classify recurrence risk. Dataset analysis and model evaluation. Contributions welcome.

• Host: GitHub
• URL: https://github.com/nafisalawalidris/logistic-regression-model-for-breast-cancer-recurrence-prediction
• Owner: nafisalawalidris
• Created: 2023-07-23T00:19:53.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2023-07-23T00:25:57.000Z (over 1 year ago)
• Last Synced: 2023-10-07T00:25:15.038Z (over 1 year ago)
• Topics: breast-cancer, classification-model, data-analysis, data-science, healthcare, logistic-regression, machine-learning, python, recurrence-prediction, scikit-learn
• Language: Python
• Homepage:
• Size: 4.88 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Logistic Regression Model to Predict Breast Cancer Recurrence

Author: [**Nafisa Lawal Idris**]

## Scenario

Breast cancer is a leading cause of death, affecting millions of women worldwide. Early detection of recurrence can significantly improve a woman's chance of survival. This project aims to create a logistic regression model to classify patients at greater risk for breast cancer recurrence based on various attributes.

## Data Files

- `~/Projects/LogisticRegression.ipynb`

- `~/Projects/breast_cancer_data/breast-cancer.csv`

## Dataset Attributes

- **recurrence**: Whether the patient had a recurrence event. (0 - No recurrence, 1 - Recurrence)

- **age_decade**: Age of the patient at the time of diagnosis, divided into bins for each decade.

- **meno_pre**: Whether the patient has not yet reached menopause.

- **meno_lt_40**: Whether the patient was less than 40 when reaching menopause.

- **meno_ge_40**: Whether the patient was at least 40 when reaching menopause.

- **tumor_size**: The largest diameter (in millimeters) of the excised tumor.

- **inv_nodes**: The number of axillary lymph nodes containing metastatic breast cancer in a histological examination.

- **node_caps**: Whether the cancer has spread outside the lymph node capsule.

- **deg_malig**: The histological grade of the tumor. (1 - Still largely normal, 2 - Somewhat abnormal, 3 - Largely abnormal)

- **breast_left**: Whether the patient had cancer in the left breast.

- **breast_right**: Whether the patient had cancer in the right breast.

- **irradiat**: Whether the cancer has been irradiated. (0 - Not irradiated, 1 - Irradiated)

## Results

After preprocessing and training a logistic regression model on the dataset, the model achieved an accuracy of 68.97% on the validation set. The model can now be used to predict breast cancer recurrence in new patients based on their attributes.

## Usage

The provided Jupyter notebook (LogisticRegression.ipynb) contains the code used for data preprocessing, model training, and evaluation. The dataset (breast-cancer.csv) used for this project is located in the breast_cancer_data directory. You can follow the notebook's instructions to reproduce the results or modify the code for further experimentation.

## Contribution

Feel free to contribute to this project by improving the model, exploring different machine learning algorithms, or incorporating additional relevant features. Your contributions are welcome and greatly appreciated!

## License

This project is licensed under [MIT].