https://github.com/bindu-1805/pcos-detection

Detection of PCOS leveraging Machine learning and interpretation using Explainable AI
https://github.com/bindu-1805/pcos-detection

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Detection of PCOS leveraging Machine learning and interpretation using Explainable AI

• Host: GitHub
• URL: https://github.com/bindu-1805/pcos-detection
• Owner: bindu-1805
• License: mit
• Created: 2024-05-15T05:05:50.000Z (8 months ago)
• Default Branch: main
• Last Pushed: 2024-06-06T17:23:36.000Z (7 months ago)
• Last Synced: 2024-06-06T19:17:31.713Z (7 months ago)
• Language: Jupyter Notebook
• Size: 1.19 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# PCOS-detection

Polycystic Ovary Syndrome (PCOS) is a widespread endocrine disorder affecting women globally. 

* This project leveraged the power of computational algorithms trained on patient data for model prediction where the Decision tree with criterion Gini index outperformed with 88.07% accuracy, 85.29% precision, 78.37% recall and 81.69% F1 Score.

* Bagging and boosting algorithms were used to monitor their performance metrics where Gradient Boost stood out with a remarkable accuracy of 91.74%, 91.00% precision, 97.00% recall and 94.00% F1 Score.

* By optimizing the chosen parameters through Hyperparameter tuning, a notable increase in the model’s accuracy was observed.

* Three ensemble models were proposed out of which the ensemble classifier ensemble model involving bagging, boosting and single classifiers brought a significant difference of 95.41% accuracy.

* Explainable (XAI) methodologies like LIME (Local Interpretable Model-Agnostic Explanations) and SHAP (SHapley Additive exPlanations) were employed for model interpretability.