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

Jarallah, Luma Fawaz

doi:10.33899/csmj.2024.150271.1131

	Medical decision support systems for diagnosing diseases based on ensemble learning algorithms
AL-Rafidain Journal of Computer Sciences and Mathematics
Article 16, Volume 18, Issue 2, December 2024, Pages 115-120 PDF (425 K)
Document Type: Research Paper
DOI: 10.33899/csmj.2024.150271.1131
Author
Luma Fawaz Jarallah^*
College of Administration and Economics Department of Management Information Systems, Mosul University, Iraq
Abstract
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
Keywords
medical decision support; disease diagnosis; machine learning; collective learning

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