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In this paper, an approach is described for improving the quality of data generated from medical screening processes based on machine learning. The designed workflow uses data acquisition from Titmus equipment and performs data preprocessing, model training, and health record evaluation. We are proposing a design of a distributed system to realize this approach in order to bridge the gap between the cloud-native technologies and their usage for patient screening in rural or remote areas. The algorithm shows promising results and is suitable for implementation on Edge-AI, IoT, and cloud-based medical support systems.

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Automating Medical Screening Processes with Machine Learning: Improving Data Quality and Reducing Human Errors

  • 会议论文

  • 发表日期：

    2025年

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  • Strahil Sokolov,
  • Kaloyan Varlyakov,
  • Dimitar Radev

摘要

In this paper, an approach is described for improving the quality of data generated from medical screening processes based on machine learning. The designed workflow uses data acquisition from Titmus equipment and performs data preprocessing, model training, and health record evaluation. We are proposing a design of a distributed system to realize this approach in order to bridge the gap between the cloud-native technologies and their usage for patient screening in rural or remote areas. The algorithm shows promising results and is suitable for implementation on Edge-AI, IoT, and cloud-based medical support systems.