(Khon Kaen University, Thailand)(2) Elvaro Islami Muryadi
(1) Department of Community, Occupational, and Family Medicine, Faculty of Medicine, Khon Kaen University, Khon Kaen 40002, Thailand. 2) Department of Public Health, Faculty of Health Sciences, Adiwangsa Jambi University, Jambi 36138, Indonesia)(3) Irianna Futri
(Khon Kaen University, Thailand)(4) Thinzar Aung Win
(Stamford International University, Thailand)(5) Khamron Sunat
(Khon Kaen University, Thailand)(6) Tri Ratnaningsih
(Gadjah Mada University, Indonesia)*corresponding author
AbstractAnemia is a prevalent global health issue that is characterized by a deficit in red blood cells or low levels of hemoglobin. This condition is influenced by various causes, including nutritional inadequacies, chronic diseases, and genetic predisposition. The incidence of the phenomenon exhibits variation across different geographical regions and demographic groups. This pioneering research investigates the identification and classification of anemia, potentially leading to transformative advancements in the discipline. The classification of anemia encompasses four distinct groups, namely Beta Thalassemia Trait, Iron Deficiency Anemia, Hemoglobin E, and Combination. This comprehensive categorization offers clinicians a more refined and detailed comprehension of the condition. The integration of deep learning and machine learning in the Multilayer Extremely Randomized Tree Learning Machine (MERTLM) model represents a departure from traditional approaches and a significant advancement in the field of medical categorization accuracy. The MERTLM approach integrates randomized tree with multilayer extreme learning machine (M-ELM) representation learning, hence emphasizing the possibility of interdisciplinary collaboration in the field of diagnostics. In addition to its impact on anemia, artificial intelligence (AI) is playing a significant role in revolutionizing medical diagnosis by emphasizing the integration of innovative methods. This study utilizes the combined capabilities of machine learning and deep learning to improve accuracy. Notably, recent developments have resulted in an exceptional accuracy rate of 99.67%, precision of 99.60%, sensitivity of 99.47%, and an amazing F1-Score of 99.53%. This study represents a significant advancement in the field of anemia research, providing valuable insights that may be applied to intricate medical issues and enhancing the quality of patient care.
KeywordsMultilayer Extremely Randomized Tree Learning Machine; Anemia; Deep Learning; Machine Learning; Stacking Model
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DOIhttps://doi.org/10.31763/ijrcs.v4i2.1379 |
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