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  DiaResFormer Landing Page

DiaResFormer: Explainable Diabetes Prediction System

Overview

DiaResFormer is an Explainable Artificial Intelligence (XAI)-driven diabetes prediction framework developed for early diabetes screening in women. The framework combines the strengths of TabResNet and FT-Transformer architectures to capture complex feature interactions within tabular healthcare data while maintaining interpretability through multiple XAI techniques.

The system was developed using multiple diabetes datasets collected from different geographical and clinical settings and includes a web-based decision support interface for real-world usability.

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Live Demo

The application is hosted on HuggingFace Spaces: https://huggingface.co/spaces/dewanjee/DiaResFormer

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Key Features

• Hybrid TabResNet + FT-Transformer architecture (ResFormer)
• Multi-dataset diabetes prediction framework
• Synthetic data generation using Hybrid TVAE-CTGAN
• Extensive benchmarking against traditional ML and modern tabular DL models
• Multiple Explainable AI (XAI) techniques
• Cross-domain and external-domain validation
• Web-based decision support system
• Confidence score and explainability visualization support

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System Summary

Component	Description
Model	ResFormer (TabResNet + FT-Transformer)
Task	Binary diabetes prediction
Data Sources	Frankfurt, PIMA, Pabna datasets
Interface	Flask-based web application
Explainability	SHAP, PFI, ALE, Counterfactuals, Attention Rollout

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Dataset Information

Dataset	Region	Samples	Diabetic	Non-Diabetic
Frankfurt	Germany	2000	684	1316
PIMA	USA	768	268	500
Pabna	Bangladesh	465	372	93

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Data Processing Pipeline

Step	Method
Data Cleaning	Removal of invalid zero medical values
Outliers	IQR + Isolation Forest (intersection rule)
Missing Values	MICE / KNN Imputation
Scaling	Standardization
Validation	Statistical consistency checks

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Synthetic Data Generation

To address imbalance and improve robustness, multiple generative models were tested:

Method
SMOTE-ENN
TVAE
CTGAN
TabDDPM
Hybrid TVAE + CTGAN

The Hybrid TVAE–CTGAN approach produced the most realistic and balanced synthetic samples based on statistical similarity metrics (KS-test, JSD, Wasserstein distance) and TSTR evaluation.

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Model Performance

Benchmark Models

Category	Models
ML Models	Logistic Regression, Random Forest, XGBoost, LightGBM, CatBoost
DL Models	TabNet, TabResNet, SAINT, FT-Transformer, TabPFN
Proposed	ResFormer (Hybrid Model)

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Final Results

Dataset	Accuracy (ResFormer)
Frankfurt	>98%
PIMA	>98%
Pabna	>98%

The model showed consistent performance across all datasets with balanced precision, recall, and AUROC.

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Proposed ResFormer Architecture

ResFormer (TabResNet + FTTransformer Hybrid)

The proposed architecture achieved comparatively balanced and consistent performance across multiple evaluation metrics.

ResFormer combines:

• Residual learning capabilities of TabResNet
• Attention mechanisms of FT-Transformer

This hybrid design enables:

• Better feature representation
• Improved modeling of tabular relationships
• Enhanced generalization across datasets

Model Architecture

Hyperparameter	Value
Token Dimension	128
Residual Blocks	2
Transformer Blocks	2
Attention Heads	4
Dropout	0.1
Optimizer	AdamW
Learning Rate	3e-4
Batch Size	256
Epochs	150

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Explainability (XAI)

Method	Purpose
SHAP	Feature contribution analysis
PFI	Global feature importance
ALE	Feature effect visualization
Counterfactuals	“What-if” reasoning
Attention Rollout	Transformer interpretability

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Cross & External Validation

Test Type	Description
Cross-domain	Train on one dataset, test on others
External validation	Tested on unseen clinical datasets

Results showed strong generalization across different population distributions.

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Web Application

A Flask-based web system was developed for real-time prediction and explanation.

Feature	Description
Input Form	Clinical feature entry
Prediction	Diabetes risk output
Confidence Score	Model certainty
Explainability View	Feature-level interpretation

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Tech Stack

Python • Flask • PyTorch • Scikit-learn • NumPy • Matplotlib • XAI Toolkits

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Note

This system is a decision-support tool for early diabetes risk screening. It is not a replacement for clinical diagnosis.

Built With

• html
• procfile
• python