Enterprise AI Analysis
Explainable AI based cervical cancer prediction using FSAE feature engineering and H2O AutoML
This study proposes a hybrid ML framework integrating H2O AutoML with autoencoder-based feature extraction and Fisher Score-based feature selection for cervical cancer prediction. It achieves 95.24% accuracy and uses LIME/SHAP for interpretability.
Executive Impact: Key Performance Metrics
Early and accurate prediction of cervical cancer is crucial. This AI model offers high accuracy (95.24%) and interpretability using LIME/SHAP, providing actionable insights for clinicians and scalable clinical decision support.
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Deep Analysis & Enterprise Applications
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Proposed Hybrid ML Workflow
Kaggle Cervical Cancer Dataset
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Pre-processing (Cleaning, Outlier, Imbalance)
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Feature Engineering (Autoencoder, Fisher Score)
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H2O AutoML Model Training (GLM, GBM, DL, Stacked Ensembles)
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LIME/SHAP Interpretability
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Compare Model Predictions
Model Performance Spotlight
95.24% Accuracy Achieved by Deep Learning Model| Model Configuration | F1 Score | Key Benefit |
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| Baseline | 0.7500 |
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| SMOTE + FS + AE (Proposed) | 0.9361 |
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Clinical Relevance: High-Risk Case Interpretation
Context: In one high-risk case, LIME identified STI history as a dominant positive contributor, significantly increasing the prediction of risk. Conversely, contraceptive use had a moderate mitigating effect. Another instance highlighted age and smoking duration as key risk-enhancing factors.
Impact: This local explanation provides actionable insights for clinicians, aligning with established medical literature on cervical cancer risk factors.
Outcome: Enhances trust and clinical adoption by making model decisions transparent and justifiable at the individual patient level.
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Implementation Timeline & Roadmap
A phased approach ensures smooth integration and adoption of the AI prediction model within your clinical workflows.
Phase 1: Data Integration & Preprocessing (4-6 Weeks)
Securely integrate existing patient data, standardize formats, and complete initial cleaning and feature engineering based on established protocols.
Phase 2: Model Adaptation & Local Validation (6-8 Weeks)
Fine-tune the H2O AutoML framework with explainable AI components (LIME/SHAP) using a local dataset. Conduct rigorous internal validation to ensure accuracy and interpretability.
Phase 3: Clinical Pilot & Feedback (8-12 Weeks)
Deploy the model in a controlled pilot environment within a clinical department. Gather feedback from clinicians and make iterative adjustments to improve usability and clinical alignment.
Phase 4: Scalable Deployment & Monitoring (Ongoing)
Roll out the solution across the enterprise, ensuring seamless integration with EHR systems via REST APIs. Establish continuous monitoring for performance, fairness, and data drift, with periodic retraining.
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