Enterprise AI Analysis
Explainable AI based cervical cancer prediction using FSAE feature engineering and H2O AutoML
Authors: Panneerselvam Karthikeyan, I. Malaserene, E. Deepakraj
Publication: Scientific Reports | (2025) 15:40439
Executive Impact
Leveraging advanced AI for early and accurate cervical cancer prediction, this study offers significant improvements in healthcare outcomes and resource allocation, with a focus on interpretability and clinical trust.
0
Accuracy on Training Data
0
AUC (Area Under Curve)
0
Log Loss
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
This paper presents a hybrid ML framework integrating H2O AutoML with an autoencoder-based feature extraction and Fisher Score-based feature selection for cervical cancer prediction. This approach enhances model transparency and clinical trust through the use of LIME and SHAP for explainability.
Enterprise Process Flow
Exploratory Data Analysis (EDA)
→
Dimensionality Reduction (Stacked Autoencoder)
→
Feature Selection (Fisher Score)
→
Model Training (H2O AutoML)
→
Deep Learning Model Selection
→
Interpretability (LIME/SHAP)
95.24%
Accuracy on Training Data Achieved by Best Model
The framework offers a scalable solution for clinical decision support, providing actionable insights for clinicians. Early and accurate prediction is critical in improving patient outcomes and optimizing healthcare resource allocation. The integration of XAI ensures predictions align with known clinical risk factors.
| Aspect | Traditional Methods | AI-Powered (This Study) |
|---|---|---|
| Cost | High, complex | Cost-effective, scalable |
| Accuracy | Varies, human error | High (95.24%), consistent |
| Interpretability | Direct observation | LIME/SHAP explanations |
| Data Dependency | Clinical observation | Large, labeled datasets (handled by AutoML) |
This study combines autoencoders for feature extraction, Fisher Score for selection, and H2O AutoML for robust model building. The use of LIME and SHAP for interpreting complex deep learning models is a key innovation, bridging the gap between accuracy and transparency.
Ablation Study Insight
The ablation study revealed that combining SMOTE, Fisher Score, and Autoencoder (FSAE) significantly improved model performance. Removing any component led to a noticeable decrease in F1 score or AUC. For instance, the complete model (SMOTE + FS + AE) achieved an AUC of 0.9901+ and an F1 score of 0.9361, ensuring robust performance across both majority and minority classes. This highlights the critical contribution and synergy of each element.
0
Combined AUC
0
Combined F1 Score
Calculate Your Potential AI ROI
Estimate the efficiency gains and cost savings your enterprise could achieve by integrating our explainable AI solutions. Adjust the parameters to reflect your organization's context.
Estimated Annual Savings
$0
Annual Hours Reclaimed
0
Your AI Implementation Roadmap
A typical journey to integrate explainable AI for enhanced clinical decision support in a healthcare enterprise.
Phase 1: Discovery & Assessment (2-4 Weeks)
Initial consultation to understand current diagnostic workflows, data infrastructure, and specific clinical needs. Includes a feasibility study and data readiness assessment.
Phase 2: Data Engineering & Model Training (6-12 Weeks)
Secure data integration, pre-processing, and custom AI model development using H2O AutoML and advanced feature engineering. Focus on initial interpretability and validation.
Phase 3: Explainability & Clinical Validation (4-8 Weeks)
Integration of LIME/SHAP for model transparency. Rigorous clinical validation with domain experts, iterative refinement, and performance benchmarking against established metrics.
Phase 4: Deployment & Monitoring (4-6 Weeks)
Deployment of the interpretable AI model into clinical systems (e.g., EHR integration via REST APIs). Continuous monitoring, performance tracking, and periodic retraining mechanisms for sustained accuracy.
Phase 5: Scaling & Optimization (Ongoing)
Expansion of AI solution to additional departments or disease areas. Further optimization of model performance, interpretability, and efficiency based on real-world feedback and new data.
Ready to Transform Your Diagnostic Capabilities?
Book a personalized consultation with our AI experts to explore how explainable AI can enhance early disease detection and improve patient outcomes in your organization.
Ready to Get Started?
Book Your Free Consultation.
Let's Discuss Your AI Strategy!
Lets Discuss Your Needs
Select a Date
Sun
Mon
Tue
Wed
Thu
Fri
Sat