ArticleJournal of healthcare engineering2022
COVID-19 Risk Prediction for Diabetic Patients Using Fuzzy Inference System and Machine Learning Approaches.
Article in Journal of healthcare engineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed, 46 citations in OpenAlex.
- Hybrid feature selection with novel deep learning model for COVID-19 risk prediction.Scientific reports · 2026Article
- Predictive modelling in times of public health emergencies: patients' non-transport decisions during the COVID-19 pandemic.BMC emergency medicine · 2025Article
- Knowledge, attitudes, practices, and perception of COVID-19 preventive measures among adult residents of Matadi (Democratic Republic of the Congo) after the third epidemic wave.Frontiers in public health · 2024Article
- Predictive modelling of transport decisions and resources optimisation in pre-hospital setting using machine learning techniques.PloS one · 2024Article
- Impact of COVID-19 lockdown on hospital admissions for epistaxis in Germany.World journal of methodology · 2023Article
- Multi-robot task assignment for serving people quarantined in multiple hotels during COVID-19 pandemic.Quantitative imaging in medicine and surgery · 2023Article
- Coronavirus disease identification using Multi-subband feature analysis in DWT domain.Procedia computer science · 2023Article
- Predicting health insurance uptake in Kenya using Random Forest: An analysis of socio-economic and demographic factors.PloS one · 2023Article
- A novel multimodal fusion framework for early diagnosis and accurate classification of COVID-19 patients using X-ray images and speech signal processing techniques.Computer methods and programs in biomedicine · 2022Article
- Multi-Label Fundus Image Classification Using Attention Mechanisms and Feature Fusion.Micromachines · 2022Article
- Impact of smart health systems on the behavior of older adults under community healthcare.Frontiers in public health · 2022Article
- Human-Machine Interaction Methods for Minimally Invasive Surgical Robotic Arms.Computational intelligence and neuroscience · 2022Review
- COVID-19 Pandemic and the Operation of a Tertiary Hospital in Tianjin-The Impact of Internet Healthcare on Hospital Business Indicators.Frontiers in public health · 2022Article
- Compliance level toward COVID-19 preventive measures and associated factors among the Ambo University community, 2021.Frontiers in public health · 2022Article
- Predictors for adherent behavior in the COVID-19 pandemic: A cross-sectional telephone survey.Frontiers in public health · 2022Article
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Authors and funding
8 authors at 5 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
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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