# A Hybrid Ensemble Approach for Early‐Stage Diabetes Detection

## TL;DR

A new hybrid machine learning model combining XGBoost and deep neural networks improves early diabetes detection with high accuracy and generalizability.

## Contribution

A novel hybrid ensemble method using XGBoost and DNNs with soft voting for diabetes detection is proposed and validated.

## Key findings

- The hybrid model achieved 99% accuracy and 1.00 AUC on the Diabetes UCI dataset.
- It showed 91% accuracy and 0.96 AUC on a new Nepal diabetes dataset.
- The approach reduced false negatives and improved stability compared to individual models.

## Abstract

Diabetes has become a critical global health concern, particularly in regions where access to diagnostic facilities is limited. In this work, we propose a hybrid framework that combines extreme gradient boosting (XGBoost) and deep neural networks (DNNs) for early‐stage diabetes detection, using soft voting to generate the final ensemble predictions. The proposed framework was evaluated on two datasets: the widely used Diabetes UCI dataset and a newly collected dataset from Nepal. The ensemble method achieved 99% accuracy (ACC) with an area under the curve (AUC) of 1.00 on the Diabetes UCI dataset, and 91% ACC with a 0.96 AUC on the Nepal diabetes dataset, demonstrating its strong generalisability across distinct populations. Compared to individual models, the hybrid approach offered increased stability and a lower rate of false negatives, which is particularly important in clinical contexts. These findings highlight the potential of hybrid machine learning–deep learning models as cost‐effective, scalable and generalisable decision‐support tools for diabetes risk assessment.

The study presents a hybrid framework combining XGBoost and deep neural networks with soft voting to enhance early‐stage diabetes detection. This approach improves accuracy, reduces false negatives and demonstrates strong generalisability across different populations, making it a promising tool for scalable clinical decision support.