Methods for imputing missing data on coronary heart disease

Preliminary analysis is an important stage of data analysis. A significant problem is the detection of missing values, and the most difficult part is that there is no universal algorithm to resolve this problem. For each specific task, known methods, their combina-tions, modifications, or completely new approaches have to be selected. Most machine learning models cannot handle missing values, so we cannot simply ignore gaps in the data. The problem of missing data needs to be addressed during pre-processing. The simplest solution is to delete each observation containing missing values. This solution is implemented in well-known Python programming language libraries such as NumPy or Pandas. However, this approach is extreme because we lose all the useful information that may be important for data analysis. There are several main strategies for imputing missing data: replacing missing values with mean/median or mode; replac-ing with the most frequently occurring value or a constant; data imputation using the kNN algorithm; multiple imputation of missing data (MICE algorithm) ; data imputation using deep learning. We suppose several modifications of algorithms for iterative multiple imputing of mixed data represented by quantitative and qualitative features. To convert qualitative features into numerical ones, we propose our own algorithms that work with missing data and allow for the conversion back to qualitative features. Two well-known datasets on observations of coronary heart disease are considered. The following is a brief description of the data imputation algorithms. The fillnaₖcolumns method, which performs data imputation based on k complete columns. It uses a regressor or classifier depending on the column type. The fillnaₖₛortedcolumns method, which traverses columns in the order corresponding to the number of missing values. It uses a regressor or classifier depending on the column type. The fillna₂stepsᵣgclass method, which is executed in 2 steps: first by complete rows, then by complete columns. It uses a regressor or classifier depending on the column type. The fillna₂stepsᵣg method, which is executed in 2 steps: first by complete rows, then by complete columns. It only uses a regressor with value adjustment for qualitative columns based on two criteria. Two types of tests are used to analyse the approaches. In the first test, a dataset is artificially filled with gaps at random positions, imputed using different methods, and the mean square error and execution time of the algorithms are estimated. In the second test, binary classification models are trained on datasets imputed with different methods and the classification accuracy is compared. The analysis showed a time advantage for the fillna₂stepsᵣg method and improved classification model accuracy in cases of using en-coding method considering frequency and the fillna₂stepsᵣgclass imputation method. Thus, the proposed methods have shown promising results, which can serve as al-ternatives to existing methods and provide researchers with additional tools to enhance decision-making accuracy. Further, the plan is to formalize the proposed methods in the scikit-learn library ar-chitecture for unified use by researchers.