2.7. Results Processing
Results processing is a crucial stage in using artificial intelligence to analyze health status. At this stage, data is systematized and interpreted, allowing significant patterns to be identified and well-founded conclusions to be drawn. The first step is filtering raw data to eliminate errors, noise, and incorrect values, which is critical for improving the accuracy of subsequent analyses. Next, data normalization is performed to enable comparison of indicators from different sources while considering individual patient characteristics.
The following stage involves applying statistical analysis and machine learning methods to uncover correlations and dependencies between parameters. These methods help detect hidden patterns that are difficult to identify with traditional analyses. During results processing, it is important to consider the disease context, clinical history, and lifestyle features of the patient. To enhance accuracy and reliability of conclusions, various algorithms are used, such as decision trees, neural networks, and clustering.
Special attention is paid to building predictive models that allow forecasting disease development, assessing risks, and adapting therapy in real time. During processing, analysis results are visualized to facilitate interpretation by medical professionals. An important aspect is the integration of data from different test types—blood, urine, stool—which provides a comprehensive picture of health. This multifaceted approach improves diagnostic accuracy and supports more effective decision-making.
Efficient results processing requires continuous improvement of algorithms and methods, as well as automation to minimize human factors and speed up analysis. As a result, the obtained data is transformed into concrete recommendations and predictive models, significantly expanding the possibilities of a personalized approach to treatment and disease prevention.