- Stakeholder(s): Healthcare providers, oncologists, and data scientists interested in diagnostic support tools
- Business Case: As part of an initiative to enhance diagnostic capabilities, this project explores the use of machine learning algorithms to assist in the early detection of breast cancer. By providing interpretable predictions, the models can support clinicians in making informed decisions, potentially leading to better patient outcomes.
Project Details
The primary objective of this project is to develop a trained machine-learning model capable of predicting cancer diagnosis based on various diagnostic features.
Leveraging machine learning, Zenith Medical Analytics aims to explore how different cellular characteristics influence tumor size, which could contribute to a better understanding of tumor growth patterns and potential risk factors.
The model is trained on a dataset containing various features related to cardiovascular health. The webapp:(https://the-mmc-mammo-insight.streamlit.app/) is built using streamlit.
Project Requirements
Data Collection and Cleaning:
- Obtain a comprehensive dataset from reliable sources, such as the UCI Machine Learning Repository, and ensure data integrity.
- Apply the CRISP-DM process including using data cleaning techniques to remove inconsistencies, missing values, and outliers.
- Document the data collection and cleaning process for transparency and reproducibility
Exploratory Data Analysis:
- Conduct thorough exploratory data analysis to identify patterns and relationships between different health parameters and the presence of heart disease.
- Implement machine learning models for binary classification.
- Create informative visualizations, statistical summaries, and interactive charts to effectively communicate key insights.
- Determine feature importance and fine-tune the model using hyperparameter tuning.
Iterative Approach to Modeling:
- Utilize advanced machine learning techniques, including support vector machines and artificial neural networks, to identify significant predictors of breast cancer.
- Develop and compare at least five models for breast cancer diagnosis prediction.
- Clearly document the methodology and choice of evaluation metrics for each model.
Recommendations and Policy Implications:
- Use SVM Model with tuned hyperparameters: SVM and ANNs consistently offer high performance and robustness.
- Feature Selection: Focus on the most influential features for streamlined and interpretable models.
- Model Interpretability: Utilize SHAP or LIME for explaining predictions to clinicians.
- Continuous Improvement: Regularly retrain models with new data to maintain accuracy
- Propose strategies to improve breast cancer awareness, access to healthcare, and lifestyle modifications.
- Articulate the potential impact of the analysis on public health outcomes.
Documentation and Codebase:
- Provide comprehensive documentation explaining the methodology, data sources, and analytical techniques used in the project.
- Ensure the codebase is well-documented and organized to facilitate easy understanding, replication, and further development.
- Adhere to best practices for code readability, efficiency, and maintainability.
Reproducibility and Open Access:
- Structure the repository to enable easy replication of the analysis and verification of results.
- Include clear instructions on obtaining and preprocessing the necessary data for the analysis.
- Ensure the repository and its contents are publicly accessible, promoting open access to the analysis, data, and code.
Collaboration and Feedback:
- Welcome contributions from the open-source community to enhance the project with bug fixes, enhancements, and additional analyses.
- Provide guidelines and instructions for contributing, ensuring a smooth collaborative process.
- Engage with users, address inquiries, and consider feedback to improve the repository and its analysis.
- Respect privacy regulations and data protection policies while handling sensitive information.
- Safeguard the anonymity of individuals and organizations involved in the dataset.
- Clearly communicate any limitations or ethical considerations associated with the analysis.