Journal of Industrial and Systems Engineering

Journal of Industrial and Systems Engineering

Investigating the impact of missing value imputation methods on the prediction of diabetes using machine learning

Document Type : Research Paper

Authors
Hooman Pourrostami 1
Seyed Amirreza Alavi 1
Ahar Hosseeini 2
Mobina Mousapour Mamoudan 1
Fariborz Jolai 3
Amir Aghsami 4
1 School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
2 Center national health insurance, Tehran, Iran
3 tehran university
4 School of Industrial Engineering, K. N. Toosi University of Technology (KNTU), Tehran, Iran
Abstract
Diabetes poses significant challenges due to its prevalence and the potential consequences of inaccurate or delayed diagnosis. This study focuses on enhancing prediction reliability to mitigate such risks. Initially, it identifies diabetes-related factors through correlation analysis with the target variable and implements models to address missing data. Subsequently, various imputation methods including CART, GMM, and RFR are employed to evaluate these factors. Results from each imputation scenario inform the selection of the most effective method. The study then employs ensemble algorithms like AdaBoost, Bagging, Gradient Boosting, and RF to enhance classification model accuracy. Further refinement is achieved by optimizing hyper-parameters through grid search. Evaluation involves comparing model predictions with those of medical professionals to assess accuracy. The findings reveal superior performance of optimized machine learning models over human predictions, indicating potential for improved diagnosis accuracy and reduced medical errors. This research contributes to advancing predictive modeling in diabetes diagnosis, offering prospects for enhanced community health and reduced socioeconomic burdens.
Keywords
Diabetes
Prediction
Machine learning
Ensemble learning
Gaussian Mixture Models
Imputation methods
Subjects
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  • Receive Date 28 January 2024
  • Revise Date 02 March 2024
  • Accept Date 26 June 2024