A Novel Medical Decision Support System Using Swarm Intelligence Based Bayesian Learning Algorithm
Keywords:
Medical decision support system (MDSS), min-max normalization method, principal component analysis (PCA), swarm-optimized Bayesian learning approach (SOBLA)Abstract
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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Bijani, M., Abedi, S., Karimi, S. and Tehranineshat, B., 2021. Major challenges and barriers in clinical decision-making as perceived by emergency medical services personnel: a qualitative content analysis. BMC emergency medicine, 21, pp.1-12.
Walker, K., Yates, J., Dening, T., Völlm, B., Tomlin, J. and Griffiths, C., 2023. Quality of life, wellbeing, recovery, and progress for older forensic mental health patients: a qualitative investigation based on the perspectives of patients and staff. International Journal of Qualitative Studies on Health and Well-being, 18(1), p.2202978.
Taranath, N.L., Roopashree, H.R., Yogeesh, A.C., Darshan, L.M. and Subbaraya, C.K., 2022. Medical decision support system using data mining. In Predictive Modeling in Biomedical Data Mining and Analysis (pp. 49-64). Academic Press.
Sutton, R.T., Pincock, D., Baumgart, D.C., Sadowski, D.C., Fedorak, R.N. and Kroeker, K.I., 2020. An overview of clinical decision support systems: benefits, risks, and strategies for success. NPJ digital medicine, 3(1), p.17.
Kukhtevich, I.I., Goryunova, V.V., Goryunova, T.I. and Zhilyaev, P.S., 2020, August. Medical decision support systems and semantic technologies in healthcare. In Russian Conference on Digital Economy and Knowledge Management (RuDEcK 2020) (pp. 370-375). Atlantis Press.
Kukharova, T.V., Utkin, V.A. and Pershin, I.M., 2021, January. Modeling of a Decision Support System for a Psychiatrist Based on the Dynamics of Electrical Conductivity Parameters. In 2021 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (ElConRus) (pp. 975-978). IEEE.
Moulaei, K. and Bahaadinbeigy, K., 2022. Diagnosing, Managing, and Controlling COVID-19 using Clinical Decision Support Systems: A Study to Introduce CDSS Applications. Journal of Biomedical Physics & Engineering, 12(2), p.213.
Yahyaoui, A., Jamil, A., Rasheed, J. and Yesiltepe, M., 2019, November. A decision support system for diabetes prediction using machine learning and deep learning techniques. In 2019 1st International Informatics and software engineering conference (UBMYK) (pp. 1-4). IEEE.
Jing, X., Min, H., Gong, Y., Sittig, D.F., Biondich, P., Robinson, D., Law, T., Wright, A., Nøhr, C., Faxvaag, A. and Rennert, L., 2022. A systematic review of ontology-based clinical decision support system rules: usage, management, and interoperability. medRxiv, pp.2022-05.
Gatta, R., Vallati, M., Dinapoli, N., Masciocchi, C., Lenkowicz, J., Cusumano, D., Casá, C., Farchione, A., Damiani, A., van Soest, J. and Dekker, A., 2019. Towards a modular decision support system for radionics: A case study on rectal cancer. Artificial intelligence in medicine, 96, pp.145-153.
Kudina, O. and de Boer, B., 2021. Co‐designing diagnosis: Towards a responsible integration of Machine Learning decision‐support systems in medical diagnostics. Journal of Evaluation in clinical practice, 27(3), pp.529-536.
Richard, A., Maytag, B., Talbot, F., Tsoukias, A. and Meinard, Y., 2020. What does it mean to provide decision support to a responsible and competent expert? The case of diagnostic decision support systems. EURO Journal on decision processes, 8(3-4), pp.205-236.
Downie, A.S., Hancock, M., Abdel Shaheed, C., McLachlan, A.J., Kocaballi, A.B., Williams, C.M., Michaleff, Z.A. and Maher, C.G., 2020. An electronic clinical decision support system for the management of low back pain in community pharmacy: development and mixed methods feasibility study. JMIR medical informatics, 8(5), p.e17203.
Liu, Y., Wang, Z., Ren, J., Tian, Y., Zhou, M., Zhou, T., Ye, K., Zhao, Y., Qiu, Y. and Li, J., 2020. A COVID-19 risk assessment decision support system for general practitioners: design and development study. Journal of medical Internet research, 22(6), p.e19786.
Peiffer-Smadja, N., Poda, A., Ouedraogo, A.S., Guiard-Schmid, J.B., Delory, T., Le Bel, J., Bouvet, E., Lariven, S., Jeanmougin, P., Ahmad, R. and Lescure, F.X., 2020. Paving the way for the implementation of a decision support system for antibiotic prescribing in primary care in West Africa: preimplementation and co-design workshop with physicians. Journal of Medical Internet Research, 22(7), p.e17940.
Nopour, R., Shanbehzadeh, M. and Kazemi-Arpanahi, H., 2021. Developing a clinical decision support system based on the fuzzy logic and decision tree to predict colorectal cancer. Medical journal of the Islamic Republic of Iran, 35, p.44.
Raji-Lawal, H.Y., Akinwale, A.T., Folorunsho, O. and Mustapha, A.O., 2020. Decision support system for dementia patients using intuitionistic fuzzy similarity measure. Soft Computing Letters, 2, p.100005.
Yoo, J., Lee, J., Min, J.Y., Choi, S.W., Kwon, J.M., Cho, I., Lim, C., Choi, M.Y. and Cha, W.C., 2022. Development of an Interoperable and Easily Transferable Clinical Decision Support System Deployment Platform: System Design and Development Study. Journal of Medical Internet Research, 24(7), p.e37928.
Petrauskas, V., Jasinevicius, R., Damuleviciene, G., Liutkevicius, A., Janaviciute, A., Lesauskaite, V., Knasiene, J., Meskauskas, Z., Dovydaitis, J., Kazanavicius, V. and Bitinaite-Paskeviciene, R., 2021. Explainable artificial intelligence-based decision support system for assessing the nutrition-related geriatric syndromes. Applied Sciences, 11(24), p.11763.
Arulanthu, P. and Perumal, E., 2020. An intelligent IoT with cloud centric medical decision support system for chronic kidney disease prediction. International Journal of Imaging Systems and Technology, 30(3), pp.815-827.
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