In order to successfully build machine learning models, we need as many annotated examples as possible such that new unlabeled examples can be classified into correct classes with the greatest possible reliability. These can be obtained from an expert in the field, who first collects the data, marks it accordingly and then sends it to us. On the basis of the annotated data and machine learning methods, we predict classes for new examples and address the problem of obtaining credible and quality feedback for our predictions. For secure data transfer, we describe a new way of using blockchain technologies as middleware for immutable reference between users to ensure that all users are synchronized and can check at any time that the information stored on the blockchain is still correct. We add steps of obtaining feedback and updating models to classical machine learning methods, and present challenges and possible solutions to motivate users to provide honest and quality feedback. Based on the reliability of our predictions, we suggest a way of assessing users and their responses. We build a classifier on medical data and, based on the calculated assessment of doctors, present three ways of enriching the learning examples and updating the classifier, two of which prove to be successful.
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