  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.
