An Innovative Imputation and Classification Approach for Accurate Disease Prediction
This work addresses the challenge of missing data in medical records for disease prediction, but it appears incremental as it builds on existing imputation and classification methods.
The paper tackles the problem of missing attribute values in medical datasets by proposing a novel imputation approach based on clustering and dimensionality reduction, which improves classification accuracy by enabling single-label assignment instead of multi-label assignment in a case study.
Imputation of missing attribute values in medical datasets for extracting hidden knowledge from medical datasets is an interesting research topic of interest which is very challenging. One cannot eliminate missing values in medical records. The reason may be because some tests may not been conducted as they are cost effective, values missed when conducting clinical trials, values may not have been recorded to name some of the reasons. Data mining researchers have been proposing various approaches to find and impute missing values to increase classification accuracies so that disease may be predicted accurately. In this paper, we propose a novel imputation approach for imputation of missing values and performing classification after fixing missing values. The approach is based on clustering concept and aims at dimensionality reduction of the records. The case study discussed shows that missing values can be fixed and imputed efficiently by achieving dimensionality reduction. The importance of proposed approach for classification is visible in the case study which assigns single class label in contrary to multi-label assignment if dimensionality reduction is not performed.