Early or Late Transplantation in Congenital Nephrotic Syndrome: Which is Effective for Optimal Growth?

Aksoy, Gülşah Kaya; Koyun, Mustafa; Çomak, Elif; Akman, Sema

doi:10.1016/j.transproceed.2019.01.191

Cited by 1 publication

(1 citation statement)

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“…The disease of nephrotic syndrome in children can run a frequently relapsing course, and consequently brings great harm to healthiness and growth of children, e.g. risk of acute kidney injury and growth retardation [4], [5]. Riar et al [6] studied the lifetime prevalence of allergies in childhood nephrotic syndrome and discovered that two-thirds of children with nephrotic syndrome at presentation have allergic symptoms and asthma.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Negative Conversion Days of Childhood Nephrotic Syndrome Based on PCA and BP-AdaBoost Neural Network

Luo

BeiHong

2019

IEEE Access

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The prognosis of childhood nephrotic syndrome directly hinges on the accurate prediction of negative conversion days (NCDs). Therefore, this paper designs a hybrid approach of principal component analysis (PCA) and backpropagation (BP)-adaptive boosting (AdaBoost) neural network (NN), and applies the method to predict the NCDs for children with nephrotic syndrome. Specifically, PCA method was employed for dimension reduction. Six principal components were extracted from multiple physiological features, and taken as input variables of three-tiered neural networks. The boosted predictor of BP-AdaBoost model, together with three predictors of BP NN, support vector machine (SVM) and radial basis function (RBF) NN, was trained for NCDs prediction. The experimental results show that the predictor of BP-AdaBoost NN achieves the mean absolute error of 0.2334, the mean relative error of 3.2789%, the SD of 4.6804 and the RSD of 50.8053% in NCDs prediction, and it outperforms other predictors of BP NN, SVM and RBF NN on both accuracy and precision. Furthermore, comparison experiments are conducted on PCA processed testing data and raw testing data for BP-AdaBoost NN and demonstrate the excellent effect of PCA. The hybrid approach of PCA and BP-AdaBoost NN is simple and reliable for NCDs prediction of childhood nephrotic syndrome, and it can help pediatricians prognose childhood nephrotic syndrome accurately and further provide patients with better care and treatment.INDEX TERMS Childhood nephrotic syndrome, negative conversion days (NCDs), principal component analysis (PCA), backpropagation (BP)-adaptive boosting (AdaBoost) neural network (NN).

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Section: Introductionmentioning

confidence: 99%