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


Mohd Zebaral Hoque, Jesmeen and Hossen, Md. Jakir and Sayeed, Md. Shohel and Khan, Chy. Mohammed Tawsif and Ganesan, Jaya and Emerson Raja, Joseph (2022) Automatic missing value imputation for cleaning phase of diabetic’s readmission prediction model. International Journal of Electrical and Computer Engineering (IJECE), 12 (2). p. 2001. ISSN 2088-8708

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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.

Item Type: Article
Uncontrolled Keywords: Data processing, classification algorithms, cleaning, data analytics, feature selection
Subjects: Q Science > QA Mathematics > QA440-699 Geometry. Trigonometry. Topology
Divisions: Faculty of Engineering and Technology (FET)
Faculty of Information Science and Technology (FIST)
Depositing User: Ms Nurul Iqtiani Ahmad
Date Deposited: 03 Feb 2022 02:40
Last Modified: 06 Mar 2023 06:41


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