Simulated datasets for population dynamics of sickle cell anaemia

Edeki, S.O.; Akanbi, Olumuyiwa O.

doi:10.1016/j.dib.2017.12.006

Cited by 1 publication

(1 citation statement)

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“…The SCD attributes adapted from Khalaf et al (2016) in Table 4 were enhanced for consideration with Age, Educational Background and Location items. Edeki and Akanbi (2017) used Monte Carlos Simulation (MCS) Technique to complement the Physical Simulation Smith's Statistical (PSSS) package in simulating data with respect to Sickle Cell Anaemia (SCA) in order to examine the mathematical inheritance formation of the SCA disease. Monte Carlo Random Number Generation technique has been used in this research to generate dataset using the fifteen attributes of Table 5.…”

Section: Training Data For Sickle Cell Diseasementioning

confidence: 99%

Applying Neural Network-Based Approach to Sickle Cell Disease-Related Pain Classification

Omogbadegun¹,

Ogundele²,

Oladipupo³

2019

Journal of Computer Science

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Nigeria accounts for 50% of Sickle Cell Disease (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)). 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. Vasoocclusion results from intermittent and recurrent acute SCD pain episodes. Pain management at the Emergency Department for vaso-occulsive crisis for patients with SCD has been obnoxious. 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 neural network was trained with the SCD datasets features 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.

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Section: Training Data For Sickle Cell Diseasementioning

confidence: 99%