## 4 Discussion  
The results of the presented research work share a common goal and foundation. As a system of interconnected models, they enable the optimization of diagnostic laboratory net \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Network ID** & **Network Name** & **Training** & **Testing** & **Validation** & **Learning** & **Error Function** & **Hidden** & **Output** \\
2 & MLP 9-13-2 & 99.13\% & 94.52\% & 98.63\% & BRGS 92 & SOS & Tanh & Exponential \\
4 & MLP 9-12-2 & 100.00\% & 100.00\% & 97.26\% & BRGS 71 & Entropy & Tanh & Softmax \\
5 & MLP 9-13-2 & 99.13\% & 93.15\% & 98.63\% & BRGS 25 & Entropy & Exponential & Softmax \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The best network architectures. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Network** & **MA** & **In(MMe)** & **Sqrt(MA)** & **AoN** & **In(MA)** & **sqrt(MMe)** & **MM.median** & **ln(x).r** & **sqrt(x).r** \\ \hline
2.MLP 9-13-2 & 26,960.4 & 255 & 289.3 & 6 & 13 & 56.5 & 42.1 & 1.3 & 1.1 \\
4.MLP 9-12-2 & 443.4 & 167.8 & 41.6 & 120.9 & 92.4 & 33.7 & 19.1 & 3.8 & 2.2 \\
5.MLP 9-13-2 & 4.2 & 14.3 & 1.7 & 2.7 & 5.9 & 4.1 & 1.2 & 1 & 1.1 \\ Average & 9136 & 145.7 & 110.9 & 43.2 & 37.1 & 31.4 & 20.8 & 2 & 1.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Sensitivity analysis of the best network architectures. work operations. They implement the functional assumptions of multiple analytical models, collectively referred to as CM4 QC--Computational Model for Quality Control. However, a significant distinction among the described models lies in their shared characteristic--instead of the need to power each functionality with a separate database obtained by specialists from different departments, we introduce a common data source. Additionally, these data comprise the fundamental raw material for laboratories--information derived from real patient test results. In this way, machine learning models facilitate the rapid and cost-effective improvement of quality management processes using readily available resources. The concept of the system is summarized in Figure 10.  
As sub-models of the system, we identify the following:  
* SOM networks (patient segmentation), which enable the creation of predictive models for the number of orders for each profile separately. * Exponential smoothing was employed for prediction. * Simple multilayer perceptrons (MLPs) were utilized to predict the need for analyzer calibration. * Logistic regression was applied to predict the moment of device filling. * Decision trees were used for classification. As noted, a variety of straightforward machine learning techniques rooted in data from research results were employed. The achieved results, characterized by high accuracy and reliability, enable effective decision making. Such an information flow structure yields simple alerts for matters such as device fill levels, calibration needs, or order volume for reagent inventory management. However, the decisions obtained enable the more effective and faster management of individual laboratory centers. The implemented machine learning models have increased laboratory efficiency. The demonstration included the accuracy of alert predictions, measured by the percentage agreement with load predictions, for ten analyzers experiencing the highest workloads. This evaluation covered 30 randomly selected daily runs for the years 2021 and 2022. For each analyzer, accuracy surpassed 80%, achieving an overall accuracy of 81.72%. Notably, during the tests, each analyzer exhibited a time reduction exceeding 10%. This resulted in an average decrease in working time under maximum load by 11.98%. These are tangible time savings, and most importantly, decisions are made based on 'live' data already possessed by the company. Figure 10: Examples of machine learning algorithms results from individual tasks in the research. Practical signs of laboratory performance improvement could be observed after the introduction and implementation of the proposed enhancements into production practice.