# Prediction of Changes in Blood Parameters Induced by Low-Frequency Ultrasound
## 5 Conclusions  
In this study, we conducted an evaluation of alterations in blood analysis data caused by the influence of ultrasound. Using an ANOVA and non-parametric Kruskal-Wallis statistical analyses, we confirmed significant changes in blood parameters due to exposure to 180 or 90 s of high- and medium-intensity ultrasound signals. The experimental results reveal that ultrasound signals have a statistically significant impact (\(p\)-value \(<\) 0.05) on multiple blood parameters, with MCH emerging as the most sensitive among them, alongside RBC, HTC, MCHC, GRAN, and MID%. In contrast, RDW values, with a \(p\)-value of 0.990, exhibited minimal changes even under high-intensity signals (180 H and 90 H).  
To predict PLT (platelet) counts, five distinct machine learning algorithms (decision trees (DT), random forest (RF), an artificial neural network (ANN), linear regression (LR), and support vector regression (SVR)) were employed in the study. The highest prediction errors were observed for high-intensity signals, i.e., 90 H and 180 H, while the most accurate predictions were observed for medium-intensity signals (90 M and 180 M). The three-layer ANN consistently produced the highest errors, with an average RMSE of 72.47 across all six different ultrasound signals, while the LR, DT, and RF models had average errors of 55.50, 47.61, and 44.39, respectively. The SVR model had the best prediction accuracy, with an average RMSE of 32.06 (MAPE = 10.34%). The lowest prediction error (MAPE), at 6.57%, was achieved when predicting PLT values exposed to the 90 M signal, while the lowestaccuracy, at 17.32%, was observed when predicting values exposed to the 180 H signal. It is essential to note that the prediction of PLT values has shown that PLT, PCT, and GRAN values are the blood factors with the most substantial impact on the predictive accuracy of PLT values.  
Ultrasound application led to a dissociation of aggregated erythrocytes into single erythrocytes, while platelets, on the other hand, were more rapidly activated, but their activity was lower after induction with epinephrine. The dissociation of erythrocytes from the aggregated to the single state results in an increase in hemoglobin carrier gas, which allows therapeutic support for patients with pulmonary hypertension. The aggregation of platelets increases blood coagulation, which is useful for wound healing. As a result of this research, faster statistical and artificial intelligence methods have been proposed to speed up the analysis and interpretation of blood parameters, allowing us to avoid the mistakes of inexperienced analysts and to take timely actions to improve human health.  
Conceptualization, V.O.; methodology, V.O., V.L., E.S. and V.T.; formal analysis, V.O. and A.T.; investigation, V.O. and V.J.; resources, V.O. and E.S.; data curation, E.S.; statistical data analysis, A.P.-T. and M.V.; machine learning model realization, A.P.-T.; writing--original draft preparation, V.O., A.P.-T. and L.K.; writing--review and editing, V.O., A.P.-T. and L.K.; visualization, V.O. and A.P.-T.; supervision, V.O. All authors have read and agreed to the published version of the manuscript.  
This research was funded by European Regional Development Fund (project No 01.2.2-LMT-K-718-05-0076) under grant agreement with the Research Council of Lithuania (LMTLT). Funded as European Union's measure in response to COVID-19 pandemic.  
The study with human pulmonary arteries was conducted according to the principles defined in the Declaration of Helsinki [37]. Permission to perform this study was obtained from the local institutional review board of the Kaunas Regional Biomedical Research Ethics Committee (No. 2022-03-10 Nr. BE-2-39).  
The datasets used and analyzed during the current study are available from the corresponding authors on reasonable request.  
The authors declare no conflict of interest.