Abstract
In this review, we present a framework that will enable us to obtain increased accuracy of computer diagnosis in medical patient checkups. To some extent, a new proposition for medical data analysis has been built based on medical data preprocessing. The result of such preprocessing is transformation of medical data from descriptive, semantic form into parameterized math form. A proper model for digging of hidden medical data properties is presented as well. Exploration of hidden data properties achieved by means of preprocessing creates new possibilities for medical data interpretation. Diagnosis selectivity has been increased by means of parameterized illnesses patterns in medical databases.
Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
Research funding: None declared.
Employment or leadership: None declared.
Honorarium: None declared.
Competing interests: The funding organization(s) played no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the report for publication.
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