An enhanced machine learning framework for Type 2 diabetes classification using imbalanced data with missing values

Roy, Kumarmangal and Ahmad, Muneer and Waqar, Kinza and Priyaah, Kirthanaah and Nebhen, Jamel and Alshamrani, Sultan S. and Raza, Muhammad Ahsan and Ali, Ihsan (2021) An enhanced machine learning framework for Type 2 diabetes classification using imbalanced data with missing values. Complexity, 2021. ISSN 1076-2787, DOI

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Diabetes is one of the most common metabolic diseases that cause high blood sugar. Early diagnosis of such a condition is challenging due to its complex interdependence on various factors. There is a need to develop critical decision support systems to assist medical practitioners in the diagnosis process. This research proposes developing a predictive model that can achieve a high classification accuracy of type 2 diabetes. The study consisted of two fundamental parts. Firstly, the study investigated handling missing data adopting data imputation, namely, median value imputation, K-nearest neighbor imputation, and iterative imputation. Consequently, the study validated the implications of these imputations using various classification algorithms, i.e., linear, tree-based, and ensemble algorithms, to see how each method affected classification accuracy. Secondly, Artificial Neural Network was employed to model the best performing imputed data, balanced with SMOTETomek ensuring each class is represented fairly. This approach provided the best accuracy of 98% on the test data, outperforming accuracies achieved in prior studies using the same dataset. The dataset used in this study is concerned with gender and population. As a prospect, the study recommends adopting a larger population sample without geographic boundaries. Additionally, as the developed Artificial Neural Network model did not undergo any specific hyperparameter tuning, it would be interesting to explore tuning on top of normalized data to optimize accuracy further.

Item Type: Article
Funders: Taif University, Taif, Saudi Arabia [TURSP-2020/215], Faculty of Computer Science and Information Technology, the University of Malaya [PG035-2016A]
Uncontrolled Keywords: Neural-networks
Subjects: Q Science > Q Science (General)
Q Science > QA Mathematics
T Technology > T Technology (General)
Divisions: Faculty of Computer Science & Information Technology
Depositing User: Ms Zaharah Ramly
Date Deposited: 16 Aug 2022 01:05
Last Modified: 16 Aug 2022 01:05

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