[Paper Review] Deep Neural Network Based Ensemble learning Algorithms for the healthcare system (diagnosis of chronic diseases)

Jafar Abdollahi, Babak Nouri-Moghaddam+1 more|arXiv (Cornell University)|Mar 15, 2021

Artificial Intelligence in Healthcare20 citations

TL;DR

This paper proposes a deep neural network-based ensemble learning framework for diagnosing chronic diseases such as diabetes, heart disease, and cancer. By combining multiple deep neural networks using ensemble strategies on real UCI healthcare datasets, the method achieves 98.5%, 99%, and 100% accuracy for diabetes, heart disease, and cancer classification, respectively, demonstrating superior performance over baseline models.

ABSTRACT

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.

Motivation & Objective

• To improve the accuracy and reliability of chronic disease diagnosis using machine learning in healthcare.
• To investigate the effectiveness of deep neural network-based ensemble learning for classifying chronic diseases.
• To evaluate and compare the performance of ensemble models against baseline classification algorithms on real-world healthcare data.
• To identify key challenges in deploying machine learning for chronic disease prediction in clinical settings.

Proposed method

• The study employs a hybrid ensemble learning approach combining multiple deep neural networks to improve classification performance.
• Models are trained on real-world UCI datasets for diabetes, heart disease, and cancer patients.
• Ensemble strategies aggregate predictions from individual deep neural networks to enhance robustness and accuracy.
• Hyperparameter tuning and cross-validation are used to optimize model performance across all datasets.
• The framework leverages deep learning architectures such as fully connected and feedforward networks for feature extraction and classification.
• Performance is evaluated using standard metrics including accuracy, precision, recall, and F1-score.

Experimental results

Research questions

• RQ1 Can a deep neural network-based ensemble learning approach outperform traditional machine learning models in diagnosing chronic diseases?
• RQ2 What level of accuracy can be achieved using ensemble deep learning on real healthcare datasets for diabetes, heart disease, and cancer?
• RQ3 How do different ensemble strategies impact the generalization and robustness of disease prediction models?
• RQ4 What are the key challenges in applying deep learning ensembles to clinical diagnosis systems?

Key findings

• The proposed deep neural network-based ensemble model achieved 98.5% accuracy in diagnosing diabetes on the UCI dataset.
• For heart disease classification, the model reached 99% accuracy, outperforming baseline algorithms.
• The model achieved perfect 100% accuracy in classifying cancer cases, indicating strong predictive capability.
• Ensemble learning significantly improved model stability and generalization across all three chronic disease datasets.
• The study identifies data imbalance, model interpretability, and clinical deployment as major challenges in real-world application.