Medical decision support systems for diagnosing diseases based on ensemble learning algorithms

Diagnosing diseases in humans is the first step in treating diseases, and knowing it is important to determine treatment and deal with the disease in the correct way. Diagnosis is made in medical institutions using available tools and specialists in each medical field to determine the problem presented by the patient. Modeling and analysis of medical data is important in healthcare and social applications in areas related to disease prediction and diagnosis. The model selection strategy is an important determinant of the performance and acceptance of a medical diagnostic decision support system. This paper proposes a stacked learning model derived from multiple ensembles learning algorithms, including Random Forest, Catboost and XGBoost. To determine the effectiveness of the model, it was tested using eight data sets covering different diseases to help make disease diagnosis decisions. The results show that the proposed model generally outperforms individual machine learning models in terms of accuracy