# Deep Neural Network Based Ensemble learning Algorithms for the healthcare system (diagnosis of chronic diseases)

arXiv: https://arxiv.org/abs/2103.08182 · HTML: https://mlsanity.com/paper/2103.08182/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 144 references that are in the map.
- First posted on arXiv 15 Mar 2021.

## Builds on (most cited first, 6 of 6)

- [Semi-Supervised Learning with Deep Generative Models](https://mlsanity.com/paper/1406.5298/) (2014)
- [Semi-Supervised Learning with Ladder Networks](https://mlsanity.com/paper/1507.02672/) (2015)
- [The Relative Performance of Ensemble Methods with Deep Convolutional Neural Networks for Image Classification](https://mlsanity.com/paper/1704.01664/) (2017)
- [Boosting Combinatorial Problem Modeling with Machine Learning](https://mlsanity.com/paper/1807.05517/) (2018)
- [Improved prediction accuracy for disease risk mapping using Gaussian Process stacked generalisation](https://mlsanity.com/paper/1612.03278/) (2016)
- [Fast and accurate sentiment classification using an enhanced Naive Bayes model](https://mlsanity.com/paper/1305.6143/) (2013)

## Built on by (most cited first, 5 of 5)

- [Uncertainty Quantification for Machine Learning in Healthcare: A Survey](https://mlsanity.com/paper/2505.02874/) (2025)
- [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)

learning algorithms. In this paper, we review the classification algorithms used in the health care system (chronic diseases) and present the neural network-based Ensemble learning method. We briefly describe the commonly used algorithms and describe their critical properties. Materials and Methods: In this study, modern classification algorithms used in healthcare, examine the principles of these methods and guidelines, and to accurately diagnose and predict chronic diseases, superior machine learning algorithms with the neural network-based ensemble learning Is used. To do this, we use experimental data, real data on chronic patients (diabetes, heart, cancer) available on the UCI site. Results: We found that group algorithms designed to diagnose chronic diseases can be more effective than baseline algorithms. It also identifies several challenges to further advancing the classification of machine learning in the diagnosis of chronic diseases. Conclusion: The results show the high performance of the neural network-based Ensemble learning approach for the diagnosis and prediction of chronic diseases, which in this study reached 98.5, 99, and 100% accuracy, respectively.