A Novel Medical Decision Support System Using Swarm Intelligence Based Bayesian Learning Algorithm


  • Preethi, Zeeshan Ahmad Lone, Ansari Mehrunnisa Hafiz, Trapty Agarwal, Saniya Khurana


Medical decision support system (MDSS), min-max normalization method, principal component analysis (PCA), swarm-optimized Bayesian learning approach (SOBLA)


The use of Machine Learning (ML) methods may be beneficial at the clinical and diagnostic levels of medical decision-making. A foundation for ML is provided by feature selection algorithms. In a medical setting, feature selection may be used to rapidly and efficiently identify the health-related qualities that are most distinctive from the original feature collection. The two primary objectives of feature selection algorithms are to determine the properties of data classes that are most relevant and to enhance classification performance. In addition to assisting lower the general measurement of the dataset, feature selection also aids in determining which features are most important. Therefore, we provide a unique ML-based approach in this study. The dataset is first gathered and prepared using the min-max normalization approach. The features are selected using principal component analysis (PCA). Using a novel swarm-optimized Bayesian learning approach (SOBLA), accuracy is used to evaluate the effectiveness of various feature subsets. Experimental results show that the performance of the proposed method performs better when compared to conventional methods. The outcomes of this study suggest interventions with the potential to enhance the quality of healthcare decision-making about certain healthcare procedures.


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How to Cite

Zeeshan Ahmad Lone, Ansari Mehrunnisa Hafiz, Trapty Agarwal, Saniya Khurana, P. . (2024). A Novel Medical Decision Support System Using Swarm Intelligence Based Bayesian Learning Algorithm. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 1095–1101. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5509



Research Article