# Prediction of Changes in Blood Parameters Induced by Low-Frequency Ultrasound
## 3 Results
### Platelet Number Prediction  
Ongoing efforts are underway to predict platelet (PLT) values based on the influence of ultrasound signals, their duration, and intensity. Machine learning algorithms are well suited to PLT value predictions. Among the various algorithms used for regression problems, this study employed the five most popular ones. Based on the experimental results of 10 tests, the support vector regression (SVR) algorithm with a linear kernel was found to be the most accurate algorithm, as determined by the root mean square error (RMSE) metric.  
Considering the results of all the machine learning algorithms, we can see that the highest error values are obtained with the three-layer _ANN_, with an average _RMSE_ value of 72.47 for all six different ultrasound signals (Table 5). The largest prediction errors are observed for the high-impact signals, i.e., 90 H and 180 H, while the best prediction results are obtained for the PLT values after exposure to medium signals (90 M and 180 M). The same tendency can be seen with the remaining models. The \(LR\), \(DT\), and \(RF\) models have an average error of 55.50, 47.61, and 44.39, respectively, and \(SVR\) demonstrated the best prediction accuracy with an average _RMSE_ of 32.06 (_MAPE_ = 10.34%, Figure 7).  
Figure 8 shows the results of the \(SVR\) model's prediction of the PLT value (in terms of the _MAPE_ value) using different ultrasound signals. The best accuracy of 6.57% was obtained when predicting PLT values after exposure to the 90 M signal, while the worst accuracy of 17.32% was obtained when predicting the results after exposure to the 180 H signal. It can be assumed that the effect of a longer and stronger ultrasound signal is slightly more difficult to predict, as extreme values occur. This is also evident from the results in (a,b) of Figure 8, where the actual PLT values affected by the ultrasound signal are shown as blue dots and the predicted ones as red dots.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**ML Algorithm** & **90 W** & **180 W** & **90 M** & **180 M** & **90 H** & **180 H** \\ \hline DT & 44.156 & 36.61 & 41.61 & 35.8 & 67.34 & 60.16 \\ RF & 47.822 & 33.68 & 34.49 & 29.75 & 67.98 & 52.63 \\ ANN & 66.04 & 51.84 & 27.04 & 41.75 & 154.24 & 93.96 \\ LR & 48.99 & 42.83 & 43.54 & 38.77 & 76.05 & 82.83 \\ SVR & 23.45 & 27.73 & 22.90 & 27.12 & 35.41 & 55.80 \\ \hline \hline \end{tabular}
\end{table}
Table 5: PLT prediction results providing the average RMSE value obtained from 10 tests for five different ML algorithms: decision trees (DT), random forest (RF), artificial neural network (ANN), linear regression (LR), and support vector regression (SVR).  
Figure 7: Prediction accuracy with an average _MAPE_.  
To assess the importance of attributes, we used the _F_-test statistical test [28], which is helpful for feature selection when there are numerous potential predictor factors, as we aim to identify a subset of variables that is most strongly associated with a response variable. The feature importance scores are computed as:  
\[Fscore=-log(p), \tag{4}\]  
where \(p\) is the probability of _F_-test.  
The top 5 importance scores of all blood parameters using the _F_-test algorithm for the prediction of the PLT value, including only the longest ultrasound signals of different strengths, i.e., 180 W, 180 M, and 180 H, are shown in Figure 9. These scores rank the attributes in order of importance, with a higher _F_-score indicating that the relevant predictor is more important. The results of the experiments showed that the baseline PLT value and the PCT are the blood factors that have the biggest impact on the predicted value of the PLT.  
Figure 8: PLT prediction results for the test data using the SVR model with actual and predicted PLT values exposed to different ultrasound signals: (**a**) 180 H, (**b**) 90 H, (**c**) 180 M, (**d**) 90 M, (**e**) 180 W and (**f**) 90 W.