Automatic missing value imputation for cleaning phase of diabetic’s readmission prediction model

Abstract

Recently, the industry of healthcare started generating a large volume of datasets. If hospitals can employ the data, they could easily predict the outcomes and provide better treatments at early stages with low cost. Here, data analytics (DA) was used to make correct decisions through proper analysis and prediction. However, inappropriate data may lead to flawed analysis and thus yield unacceptable conclusions. Hence, transforming the improper data from the entire data set into useful data is essential. Machine learning (ML) technique was used to overcome the issues due to incomplete data. A new architecture, automatic missing value imputation (AMVI) was developed to predict missing values in the dataset, including data sampling and feature selection. Four prediction models (i.e., logistic regression, support vector machine (SVM), AdaBoost, and random forest algorithms) were selected from the well-known classification. The complete AMVI architecture performance was evaluated using a structured data set obtained from the UCI repository. Accuracy of around 90% was achieved. It was also confirmed from cross-validation that the trained ML model is suitable and not over-fitted. This trained model is developed based on the dataset, which is not dependent on a specific environment. It will train and obtain the outperformed model depending on the data available.

A method for missing values imputation of machine learning datasets

“…The intuitive way to deal with missing data is the deletion of the features or instances containing missing values [5]- [7]. However, this method has risks of losing important information in datasets, and it can significantly impact classification accuracy.…”

“…Int J Artif Intell ISSN: 2252-8938  Hoque et al [5] have compared the imputation accuracy of many machine-learning-based methods. They found that adaboost classifier and linear support vector machine (SVM) are better than logistic regression (LR), and random forest (RF).…”