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. 2022 Apr 1:2022:4096950.
doi: 10.1155/2022/4096950. eCollection 2022.

COVID-19 Risk Prediction for Diabetic Patients Using Fuzzy Inference System and Machine Learning Approaches

Alok Aggarwal  1 , Madam Chakradar  1 , Manpreet Singh Bhatia  1 , Manoj Kumar  1 , Thompson Stephan  2 , Sachin Kumar Gupta  3 , S H Alsamhi  4 , Hatem Al-Dois  5
Affiliations

COVID-19 Risk Prediction for Diabetic Patients Using Fuzzy Inference System and Machine Learning Approaches

Alok Aggarwal et al. J Healthc Eng. .

Abstract

Individuals with pre-existing diabetes seem to be vulnerable to the COVID-19 due to changes in blood sugar levels and diabetes complications. As observed globally, around 20-50% of individuals affected by coronavirus had diabetes. However, there is no recent finding that diabetic patients are more prone to contract COVID-19 than nondiabetic patients. However, a few recent findings have observed that it could be at least twice as likely to die from complications of diabetes. Considering the multifold mortality rate of COVID-19 in diabetic patients, this study proposes a COVID-19 risk prediction model for diabetic patients using a fuzzy inference system and machine learning approaches. This study aimed to estimate the risk level of COVID-19 in diabetic patients without a medical practitioner's advice for timely action and overcoming the multifold mortality rate of COVID-19 in diabetic patients. The proposed model takes eight input parameters, which were found as the most influential symptoms in diabetic patients. With the help of the various state-of-the-art machine learning techniques, fifteen models were built over the rule base. CatBoost classifier gives the best accuracy, recall, precision, F1 score, and kappa score. After hyper-parameter optimization, CatBoost classifier showed 76% accuracy and improvements in the recall, precision, F1 score, and kappa score, followed by logistic regression and XGBoost with 75.1% and 74.7% accuracy. Stratified k-fold cross-validation is used for validation purposes.

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Conflict of interest statement

The authors declare that they have no conflicts of interest.

Figures

Figure 1
Figure 1
Proposed inference pipeline.
Figure 2
Figure 2
Fuzzy set membership diagrams.
Figure 3
Figure 3
Comparison of accuracy after hyper-parameter optimization.
Figure 4
Figure 4
Comparison of recall after hyper-parameter optimization.
Figure 5
Figure 5
Comparison of precision after hyper-parameter optimization.
Figure 6
Figure 6
Comparison of kappa score after hyper-parameter optimization.
Figure 7
Figure 7
Comparison of F1 score after hyper-parameter optimization.
Figure 8
Figure 8
Confusion matrices of CatBoost classifier before hyper-parameter tuning.
Figure 9
Figure 9
Confusion matrices of CatBoost classifier after hyper-parameter tuning.
Figure 10
Figure 10
ROC curve for CatBoost classifier with AUC scores.
Figure 11
Figure 11
Validation of training and cross-validation scores.
See this image and copyright information in PMC

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