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In this section, the proposed method is evaluated by simulating a neural network model using combined data from more than 10 management areas (MAs).
These 10 items were entered in the third model using combined male and female data sets.
The model using combined data was better than (p<0.05) ECO for all cancers except breast, CNS, gynaecological and haematological cancers.
The model using combined data performed better than EAR (p<0.05) for all tumour streams other than CNS, head and neck and upper gastrointestinal tumours.
Table 4 presents the detailed results of each prediction model using combined physical factors, and Table 5 presents the physical factors inserted in each final model using the wrapper-based variable selection and LASSO techniques.
For this purpose, the estimated outputs ranging from 0 to 1 were inputted to the R console (version R 2.15.2; The R Project for Statistical Computing, Vienna, Austria), and the R open source software package pROC [ 35] was used in the R console to compute the ROC curve and AUC for each model using combined predictor variables.
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The non-linear path loss model used, combined with the effect of scheduling transmissions among subcells within a cell, gives some non-linear and somewhat surprising results.
The mathematical model used combines the multi-criteria decision making theories (MCDM) together with the fuzzy sets theory.
The three-dimensional numerical model used combines the finite element method and the poroelastic displacement discontinuity method.
The model used combines the potential distribution in the electrolyte with the load unload behaviour of the double layer.
Its diagnostic performance is slightly worse than the predictor ODI3 and the SVM model designed using combined ODI parameters, i.e., ODI2 and ODI4A, but is similar to the single-variable predictors, ODI2, ODI4T, and ODI4A (Table 4).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com