• Project Execution By:

  • Danial Soleimany
  • Machine Learning Engineer
  • Student of Artificial Intelligence

• Project Supervisor:

  • Samaneh Zahedi
  • Bachelor of Radiology from Shiraz University of Medical Sciences
  • Masters Degree in Medical Physics from Jondishapour University of Medical Sciences

🧐 Objectives

1. Prediction: Predict whether a patient has cervical uterus cancer.
2. Survival Estimation: Estimate the number of days a patient will survive with cervical cancer.
3. Machine Learning Application: Apply machine learning algorithms for regression and classification tasks.
4. Feature Importance: Identify impactful features through feature selection methods.
5. Dose Rate Investigation: Investigate the relationship between dose rate and survival duration over time.

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Challenges

🤒 Challenges

1. Limited Dataset: Small number of samples challenges achieving high accuracies.
2. Outliers: Presence of outliers affecting algorithms sensitive to them.
3. Imbalanced Data: Class imbalance in target variable biasing model learning.
4. Missing Values: Handling missing values without significant data loss.

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Solutions

👩‍⚕️ Solutions

1. Hyper-parameter Tuning: Prevent overfitting or underfitting using hyper-parameter tuning.
2. RobustScaler: Mitigate outlier impact by scaling data before model training.
3. Stratified Sampling: Ensure balanced representation of classes in training and testing sets.
4. Imputation: Handle missing values with mean and mode imputation to retain sample size.

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License

This project is licensed under the MIT License - see the LICENSE file for details.

solutions, and guidance on how to get started with and contribute to the project.