Deep Neural Network Based Ensemble learning Algorithms for the healthcare system (diagnosis of chronic diseases)
This addresses improving diagnostic accuracy for chronic diseases in healthcare, but appears incremental as it applies existing ensemble methods to medical data.
The paper tackles diagnosing chronic diseases like diabetes, heart disease, and cancer using neural network-based ensemble learning, achieving high accuracies of 98.5%, 99%, and 100% on UCI datasets.
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.