# Hybrid stacked ensemble combined with genetic algorithms for Prediction of Diabetes

arXiv: https://arxiv.org/abs/2103.08186 · HTML: https://mlsanity.com/paper/2103.08186/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 95 references that are in the map.
- First posted on arXiv 15 Mar 2021.

## Built on by (most cited first, 4 of 4)

- [Evaluating LeNet Algorithms in Classification Lung Cancer from Iraq-Oncology Teaching Hospital/National Center for Cancer Diseases](https://mlsanity.com/paper/2305.13333/) (2023)
- [Recent advancement in Disease Diagnostic using machine learning: Systematic survey of decades, comparisons, and challenges](https://mlsanity.com/paper/2308.01319/) (2023)
- [A comprehensive review of deep learning in lung cancer](https://mlsanity.com/paper/2308.02528/) (2023)
- [Review of deep learning in healthcare](https://mlsanity.com/paper/2310.00727/) (2023)

## Abstract (arXiv)

Diabetes is currently one of the most common, dangerous, and costly diseases in the world that is caused by an increase in blood sugar or a decrease in insulin in the body. Diabetes can have detrimental effects on people's health if diagnosed late. Today, diabetes has become one of the challenges for health and government officials. Prevention is a priority, and taking care of people's health without compromising their comfort is an essential need. In this study, the Ensemble training methodology based on genetic algorithms are used to accurately diagnose and predict the outcomes of diabetes mellitus. In this study, we use the experimental data, real data on Indian diabetics on the University of California website. Current developments in ICT, such as the Internet of Things, machine learning, and data mining, allow us to provide health strategies with more intelligent capabilities to accurately predict the outcomes of the disease in daily life and the hospital and prevent the progression of this disease and its many complications. The results show the high performance of the proposed method in diagnosing the disease, which has reached 98.8%, and 99% accuracy in this study.