[Retracted] Analysis of Risk Factors of Neurobiological Pipeline Care and Investigation of Preventive Measures

Table 2

SMOTEC-SMO algorithm descriptions.

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Input: Number of minority class samples N; the number of clusters K; similarity threshold S; SMOTE oversampling expansion multiplier T%.

Output: SMO classification plane and prediction results.

Initialize the hypermastigote, which include the number of iterations t, the learning rate L, the hypermastigote of the recurrent neural network , , and the computational gradient of the model ;

(1) BEGIN/ Improved SMOTE Over-sampling/

(2) G = int(N  T%); //Oversampling a few class examples

(3) For i = 1 to G

(4) For I = 1 to N

(5) Compute k-NN ; //Find 5 nearest neighbors of

(6) For i = 1 to nuberattr/∗nuberattrIndicates the number of attributes/

(7) If is a numeric attribute

(8) If

(9) Rand(1)//Different generation methods for different cases of near neighbors

(10) elseif

(11) ^∗rand(0.5)

(12) endif

(13) endif

(14) endFor

(15) endFor

(16) endFor

(17)

(18) If , / If j is a continuous value /

(19) If If J is a discrete value / Clustering under-samping /

(20) Clustering divides the training data set into K clusters;

/ SMO /SMOTraining; //Training with sequential minimum optimization (SMO) algorithm

SMOProjections;

END

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