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Harnessing Hybrid Intelligent Models for Analyzing Musculoskeletal Mortality: A Data-Driven Approach |
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PP: 913-919 |
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doi:10.18576/amis/190416
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Author(s) |
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Eiman Talal Alharby,
Ashrf Althbiti,
Sultan Ahmed Almalki,
Mohammed Alshahrani,
Mohammed Al-Jabbar,
Salem Alkhalaf,
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Abstract |
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| This paper addresses the critical challenges in characterizing musculoskeletal mortality, a prevalent and serious non- communicable condition affecting nearly one in three women and one in five men over the age of 50 worldwide. Globally, musculoskeletal mortality impacts approximately 200 million women, with prevalence rates of 23.1 in women and 11.7 in men, reaching its highest incidence in Africa at 39.5. This study leverages extensive datasets and advanced computational techniques, including machine learning models such as Decision Trees (DT), Artificial Neural Networks (ANN), and a hybrid DT-ANN approach, to improve diagnosis, treatment options, and disease management. By integrating data-driven methodologies with medical imaging, this innovative approach aims to enhance diagnostic accuracy and optimize patient outcomes. Furthermore, the proposed framework facilitates personalized treatment strategies through comprehensive analyses of clinical and genetic information, reinforcing the role of cutting-edge technology in transforming musculoskeletal mortality research and healthcare practices.
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