Reasons and features of big data preprocessing in neurosurgery. Practical guideline

Abstract

Current paper presents the features of big data preprocessing, to which artificial intelligence technologies can be applied in clinical neurosurgery. At the initial stage of creating patient registries, the data are characterized by missing values and variability, which requires the use of specific preprocessing methods. Data cleaning, handling missing values, and addressing variability issues at the initial stage are critically important for subsequent analysis and the application of artificial intelligence technologies. Data preprocessing includes detecting statistical outliers, normalization, feature extraction, and feature selection. These steps are performed using methods such as complete case analysis, imputation techniques, the last observation carried forward method, and dimensionality reduction methods. The application of these methods improves the accuracy and efficiency of artificial intelligence algorithms and enhances the interpretation of results. This paper provides practical recommendations for neurosurgeons on the selection and application of data preprocessing methods

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