Imputation of Missing Values with Adaptive Elastic Net for Gene Selection in High-dimensional Data

Missing data is a problem that often arises in a variety of real-world systems. The performance of classification algorithms operating on these systems would suffer as a result. Effective imputation approaches abound to tackle this issue in case of missing data with low dimensions. In addition, one of the most common methods for concurrently doing variable selection and coefficient estimation in high-dimensional data is the penalized regression technique. However, one of the most significant problems associated with high-dimensional data is that it often includes an enormous quantity of missing data, which means that conventional imputation methods may not adequately address it. This paper proposes the imputation of missing values with the adaptive elastic net as an extension of penalized techniques to enhance gene selection and impute missing values in high-dimensional data. The effectiveness of the proposed method is evaluated by applying it to high-dimensional datasets that are taken from real-world situations with varying numbers of features, sample sizes, and percentages of missing datasets. A comparison is made between the proposed approach and various imputation-penalized methods that are currently in use for high-dimensional data. The findings of the comparison experiments reveal that the proposed technique is superior to its rivals since it achieves a better value for classification accuracy, sensitivity, and specificity than its competitors.