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case_images: Trends in Estimated T2D in Saudi Arabia (2000-2045).
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case_images: Similarly, Fig 9 indicates that SVR3 outperformed SVR1 and SVR2, offering a superior fit.
case_images: Number of patients in KAUH between (2018-2022).
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case_images: Similarly, Fig 9 indicates that SVR3 outperformed SVR1 and SVR2, offering a superior fit. Fig 3 shows that T2D onset is most common between 40 and 90 years of age.
case_images: Distribution of age for all patients.
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case_images: Based on Fig 3 the most common age group is 40 to 90 years old, as indicated by the highest number of patients in these age groups. Fig 7, the logarithmic ANN model (ANN3) demonstrated the closest fit, outperforming ANN1 and ANN2. Fig 8 highlights that the RF3 model, with the logarithmic transformation, achieved the best results, showing minimal error and a tight clustering of points around the line of perfect prediction. Similarly, Fig 9 indicates that SVR3 outperformed SVR1 and SVR2, offering a superior fit. Lastly, Fig 10 shows that DTR3 (logarithmic transformation) outperformed DTR1 and DTR2. Fig 3 shows that T2D onset is most common between 40 and 90 years of age.
case_images: Plot of actual against predicted values for the MLR models for the original response variable (y), logarithmic transformation (logy), and square root transformation (sqrt(y)).
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case_images: Fig 4 presents the actual versus predicted values plot, illustrating the predictive performance of the models. The plot shows that the MLR6 model, which utilizes the logarithm of the age at onset of T2D with interactions between variables, demonstrates the best predictive power due to the closeness of the predicted values to the actual values (Fig 4f).
case_images: Neural network architecture.. MS: Marital Status, Phy: Physical activity, TF: Type of food, HDL: High-Density Lipoprotein, TG: Triglycerides, TC: Total Cholesterol, WBC: White Blood Cells, BMI: Body Mass Index, SBP: Systolic Blood Pressure, DBP: Diastolic Blood Pressure.
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case_images: In the ANN model, significant hyperparameters were finely tuned, including 18 input variables, two hidden layers with 18 neurons each, and a single output layer, through 10-fold cross-validation to optimize the network's predictive accuracy, as shown in Fig 5.
case_images: Optimal hyperparameters for RF, SVR, and DT models through cross-validation.. The hyperparameter corresponding to the smallest RMSE presents the optimal value for each model.
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case_images: Through 10-fold cross-validation, it was revealed that the lowest RMSE was achieved when maximum features were set to 4 variables per node, as shown in Fig 6. In Fig 6, we can observe that as the number of variables selected per node increased beyond 4, the RMSE began to rise. The ideal configuration of four features per node resulted in the highest model performance, yielding the lowest RMSE of 0.010, as illustrated in Fig 6. Fig 6 illustrates the effect of varying the value of c from 0.1 to 100 on the SVR's performance. Using 10-fold cross-validation, it was observed that as the complexity parameter increased, the RMSE values steadily rose, as shown in Fig 6.
case_images: Plot of actual against predicted values for the ANN models for the original response variable (y), logarithmic transformation (logy), and square root transformation (sqrt(y)).
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case_images: Fig 7, the logarithmic ANN model (ANN3) demonstrated the closest fit, outperforming ANN1 and ANN2. Fig 8 highlights that the RF3 model, with the logarithmic transformation, achieved the best results, showing minimal error and a tight clustering of points around the line of perfect prediction.
case_images: Plot of actual against predicted values for the RF models for the original response variable (y), logarithmic transformation (logy), and square root transformation (sqrt(y)).
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case_images: Fig 7, the logarithmic ANN model (ANN3) demonstrated the closest fit, outperforming ANN1 and ANN2. Fig 8 highlights that the RF3 model, with the logarithmic transformation, achieved the best results, showing minimal error and a tight clustering of points around the line of perfect prediction.
case_images: Plot of actual against predicted values for the SVR models for the original response variable (y), logarithmic transformation (logy), and square root transformation (sqrt(y)).
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case_images: Similarly, Fig 9 indicates that SVR3 outperformed SVR1 and SVR2, offering a superior fit.
case_images: Plot of actual against predicted values for the DTR models for the original response variable (y), logarithmic transformation (logy), and square root transformation (sqrt(y)).
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case_images: Lastly, Fig 10 shows that DTR3 (logarithmic transformation) outperformed DTR1 and DTR2.
case_images: Risk factors of age at onset of T2D for MLR, ANN, RF, SVR, and DTR.
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case_images: Fig 11 illustrates the variable importance across different models MLR, ANN, RF, SVR, and DTR.
case_images: Plots of the MLR models' training and testing data in the 40-90 years age group.. (a, b) y: age at onset(c, d) y: square root of age at onset(e, f) y: logarithm of age at onset.
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case_images: Fig 12 illustrates the comparison between observed and fitted values for the MLR models, both with and without interactions, for individuals aged 40-90. The graph shows that the MLR12 model, which applies the logarithm of the age at onset of T2D along with variable interactions is the best in terms of predictive performance due to the minimal residuals between the predicted and actual values (Fig 12f).
case_images: Plots of the ANN models' training and testing data in the 40-90 years age group.. (a, b) y: age at onset(c, d) y: square root of age at onset(e, f) y: logarithm of age at onset.
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case_images: However, the RF model in Fig 13 demonstrated the highest predictive accuracy, with minimal error and the tightest clustering of points around the line of perfect prediction, showing the closest alignment between observed and predicted values.
case_images: Plots of the RF models' training and testing data in the 40-90 years age group.. (a, b) y: age at onset(c, d) y: square root of age at onset(e, f) y: logarithm of age at onset.
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case_images: Plots of the SVR models' training and testing data in the 40-90 years age group.. (a, b) y: age at onset(c, d) y: square root of age at onset(e, f) y: logarithm of age at onset.
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