SavedAdd your journal

Journals / Sigma Mühendislik ve Fen Bilimleri Dergisi / 2019 / Cilt: 37 - Sayı: 4

PARAMETER ESTIMATION TO AN ANEMIA MODEL USING THE PARTICLE SWARM OPTIMIZATION

Arshed A. AHMAD
Murat SARI
Pages
1335–1347
Publication Date
2019-12-01
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Thus, the article is believed to be beneficial especially for who are interested in biomedical models arising in various fields of medical science, especially anemia.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The aim of this study is to predict anemia from a population through biomedical variables by using the optimum linear model.
📊
Key Findings & Data:The study is conducted in terms of data set consisting of 539 subjects provided from blood laboratories.
💡
Conclusion & Impact:Thus, the article is believed to be beneficial especially for who are interested in biomedical models arising in various fields of medical science, especially anemia.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Anemia · medical model · linear model

Abstract

The aim of this study is to predict anemia from a population through biomedical variables by using the optimum linear model. A linear medical model based on biomedical variables is produced and an effective technique is used in investigating the optimum parameters of the model. To achieve this, the particle swarm optimization (PSO) algorithm have effectively been applied in predicting the parameters of the model through the biomedical variables. The study is conducted in terms of data set consisting of 539 subjects provided from blood laboratories. Optimum values of the parameters produced from the PSO algorithm are used here to obtain the more realistic model. The model based on the variables and outcomes is expected to serve as a good indicator of disease diagnosis for health providers and planning treatment schedules for their patients. Thus, the article is believed to be beneficial especially for who are interested in biomedical models arising in various fields of medical science, especially anemia.

🔖

Cite This Article

AHMAD, A. A., SARI, M. (2019). PARAMETER ESTIMATION TO AN ANEMIA MODEL USING THE PARTICLE SWARM OPTIMIZATION. Sigma Mühendislik ve Fen Bilimleri Dergisi, Cilt: 37 - Sayı: 4, 1335–1347. https://ajindex.com/articles/419129

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (16)

  1. Janz T.G., Johnson R.L., Rubenstein S.D., (2013) Anemia in the Emergency Department: Evaluation and Treatment, Emergency Medicine Practice 15, 1-5
  2. Ganesan J., Saravanabavan V., (2018) Nutritional Problems of Anaemia Disorders Among the Tea Plantation Labourers in Nilgiris District–A Geo Medical Study, International Journal of Scientific Research in Science, Engineering and Technology 4, 1360-1366
  3. Rodak B.F., Fritsma G.A., Doig K., (2007) Hematology: Clinical Principles and Applications, Elsevier Health Sciences
  4. WHO C., (2008) Worldwide Prevalence of Anaemia 1993–2005, WHO Global Database on Anaemia
  5. Kern W.F., (2002) PDQ Hematology, PMPH-USA
  6. Sari M., Cetiner B.G., (2009) Predicting Effect of Physical Factors on Tibial Motion Using Artificial Neural Networks, Expert Systems with Applications 36, 9743-9746
  7. Sari M., Gulbandilar E., Cimbiz A., (2012) Prediction of Low Back Pain with Two Expert Systems, Journal of Medical Systems 36, 1523-1527
  8. Gulbandilar E., Cimbiz A., Sari M., Ozden H., (2008) Relationship between Skin Resistance Level and Static Balance in Type II Diabetic Subjects, Diabetes Research and Clinical Practice 82, 335-339
  9. Sari M., (2007) Relationship between Physical Factors and Tibial Motion in Healthy Subjects: 2D and 3D Analyses, Advances in Therapy 24, 772-783
  10. Sari M., Tuna C., (2018) Prediction of Pathological Subjects Using Genetic Algorithms, Computational and Mathematical Methods in Medicine
  11. Cetiner B.G., Sari M., (2010) Tibial Rotation Assessment Using Artificial Neural Networks, Mathematical and Computational Applications 15, 34-44
  12. Loke Y.H., Harahsheh A.S., Krieger A., Olivieri, L.J., (2017) Usage of 3D Models of Tetralogy of Fallot for Medical Education: Impact on Learning Congenital Heart Disease, BMC Medical Education 17, 54
  13. Soler M.J., Riera M., Batlle D., (2012) New Experimental Models of Diabetic Nephropathy in Mice Models of Type 2 Diabetes: Efforts to Replicate Human Nephropathy, Experimental Diabetes Research
  14. Stephens M.H., Grey A., Fernandez J., Kalluru R., Faasse K., Horne A., Petrie K.J., (2016) 3-D Bone Models to Improve Treatment Initiation Among Patients with Osteoporosis: A Randomised Controlled Pilot Trial, Psychology & Health 31, 487-497
  15. Botesteanu D.A., Lipkowitz S., Lee J.M., Levy D., (2016) Mathematical Models of Breast and Ovarian Cancers, Wiley Interdisciplinary Reviews: Systems Biology and Medicine 8, 337-362
  16. Gilli Q., Mustapha