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
Highlights
Siddique and Adeli (2013) stated that computational Intelligence (CI) promised to advance the healthcare sector and clinical practice of disease management in diagnosis, treatment, prevention, prescription and optimization of the fast delivery to patient with these diseases. Akinwonmi (2011) confirmed Artificial Neural Network (ANN)’s connection/strength could be determined by the activation function which could be either linear or non-linear
The evaluation of the neural network was based on some certain parameters such as sensitivity, specificity, precision, the F1 score, Youden’s J statistic and the classification accuracy
The activation function was determined by the weight of the network, the gradient of the loss function fed into the network to the backpropagation to update the weights of the function in order to reduce the loss function
Summary
Siddique and Adeli (2013) stated that computational Intelligence (CI) promised to advance the healthcare sector and clinical practice of disease management in diagnosis, treatment, prevention, prescription and optimization of the fast delivery to patient with these diseases. Akinwonmi (2011) confirmed Artificial Neural Network (ANN)’s connection/strength could be determined by the activation function which could be either linear or non-linear. Akinwonmi (2011) confirmed Artificial Neural Network (ANN)’s connection/strength could be determined by the activation function which could be either linear or non-linear. The neural network adapts itself to learn and optimise to produce the desired output. Liu et al (2006) affirmed ANN has been used in healthcare sector by applying the classification methods as ANNs identify the dataset features in order to accurately diagnose the nature of diseases, pains and sicknesses. Blood vessel occlusion accompanied by painful episodes and even death are evident in SCD (Macintyre et al, 2010; Jain and Gupta, 2016; and Xu et al, 2017). People suffering from SSCA have RBC in the range 2.37-3.73 cells/mcl with value variations as in Table 1 (Jain and Gupta, 2016)
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