Omogbadegun, Zacchaeus O. and Ogundele, Isreal and Oladipupo, O. O. (2019) Applying Neural Network-Based Approach to Sickle Cell Disease-Related Pain Classification. Journal of Computer Science. pp. 861-872.
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Abstract
Sickle Cell Disease (SCD), an inherited Red Blood Cell (RBC) disorder, is characterized by anaemia, end-organ damage, unpredictable episodes of pain and early mortality. SCD affects 25% of people living in Central and West Africa causing life threatening “silent” strokes and lifelong damage. Nigeria accounts for 50% of SCD births worldwide (estimated 150,000 of 300,000 babies born with Symptomatic Sickle Cell Anaemia (SSCA) yearly, an annual infant death of 100,000 (8% of her infant mortality)) and about 2.3% of her population suffers from SCD with 40 million (25%) being healthy carriers. The number of such babies born with SSCA yearly has been estimated as 400,000 by year 2050. Healthcare resources for SCD are inadequate and the numbers of SCD are increasing daily, thereby demanding more sufficient resources. Intermittent and recurrent acute pain episodes are associated with SCD as a result of vaso�occlusion. Pain management at the Emergency Department for vaso�occulsive crisis for patients with SCD has been obnoxious. Biopsychosocial assessment and multidisciplinary pain management may be required when treating patients with frequent, painful sickle cell crises. Early and aggressive SCD-related pain management becomes a priority to improve quality of life and prevent worsening morbidities. Computational Intelligence-based framework in promoting higher-quality care and consequent increased life-expectancy in SCD patients is expedient. Monte Carlo Simulation Technique of Random Number Generation was used to generate 515 datasets for enhanced fifteen attributes of SCD. The datasets’ features of SCD were used to train the neural network according to the pain encountered in identifying and treating the patient as fast as possible. This paper provides back-propagation algorithm of Artificial Neural Network in optimizing SCD-related pain classification and treatment processes, to complementa multidisciplinary care team intervention thereby increasing the quality of life
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Computational Intelligence, Healthcare, Artificial Neural Network, Sickle Cell Disease, Vaso-Occlusive Crisis |
| Subjects: | Q Science > Q Science (General) Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Faculty of Engineering, Science and Mathematics > School of Electronics and Computer Science |
| Depositing User: | Dr ibukun Afolabi |
| Date Deposited: | 18 Jun 2021 15:22 |
| Last Modified: | 18 Jun 2021 15:22 |
| URI: | http://eprints.covenantuniversity.edu.ng/id/eprint/14716 |
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