2.5. Data Collection
The data collection process is a fundamental stage for conducting deep health analysis using artificial intelligence technologies. At this stage, a systematic gathering of diverse information sources is performed, including medical histories, laboratory test results, data from wearable devices, and electronic medical records. Particular attention is paid to ensuring completeness and accuracy of the collected data, as the quality of source information directly affects the effectiveness of subsequent analysis stages. Both structured data and unstructured data, such as medical imaging, textual reports, and audio recordings, are used to form a comprehensive picture of the patient's health.
An important task is the standardization of formats and the creation of a unified information space, which facilitates data integration from various sources and ensures their joint use in analytical models. Automation of data collection processes plays a significant role through the implementation of electronic systems and Internet of Things (IoT) devices, which enable obtaining up-to-date information in real time.
Information gathering also involves verification of data reliability and validity, elimination of duplicate or erroneous records, providing a stable foundation for analytics. These measures are necessary to minimize errors and enhance the accuracy of detecting patterns, predispositions, and early signs of possible diseases.
As a result of implementing modern data accumulation methods, a reliable database is created, enabling the application of machine learning and artificial intelligence techniques for diagnostics, prognostic analysis, and personalization of treatment recommendations.