Serious damages, morbidity, and mortality caused by diabetes can be reduced by early diagnosis and treatment. It is important to start early treatment with automatic early detection of diseases to slow down the disease process. In the healthcare system, with the contribution of artificial intelligence-based systems, cancer cells, liver disorder, tumor detection, heart disease, breast cancer, COVID-19, and diabetes, great progress has been made for automatic detection of diseases. Factors such as blood specimen collection, evaluation time, and instrumental errors can affect laboratory analysis results. Since blood cells can continue to metabolize glucose, the duration of blood glucose determination after blood is drawn is important. For this test, blood samples taken from the subjects are transported to the laboratories. Plasma glucose measurement is one of the clinical diagnostic tests and continues to be the basis of diagnostic criteria. The current diagnostic criteria used for the diagnosis of diabetes have been in place globally for almost a decade. Providing care to people with diabetes is an essential part of the effort. Diabetic patients may be at high risk for COVID-19. (2020) claimed that diabetes should be seen as a risk factor for the rapid progression and poor prognosis of COVID-19 rapid progression and poor prognosis. A study conducted in Wuhan, China, revealed that 32% of 41 people infected with COVID-19 had underlying diseases and 20% had diabetes. Mortality rates from pneumonia in people with diabetes aged 75 years and over in Hong Kong currently exceed death rates from cardiovascular disease and cancer in this age group. COVID-19 has recently become one of the serious and acute diseases that has spread all over the world, having a serious impact on the health system and the overall global economy.
As with flu-related mortality, diabetes is a significant risk factor for the negative consequences of COVID-19. It is reported that diabetes and plasma glucose levels in SARS patients are independent predictors of morbidity and mortality. Many studies have shown a higher susceptibility to certain infectious diseases in people with diabetes, possibly due to the dysregulated immune system. ĭiabetes is expected to increase significantly over the next decades. Approximately 422 million people worldwide, mostly in low- and middle-income countries, have diabetes, and diabetes is the direct cause of more than 1.6 million deaths each year. The proposed method can be used as an auxiliary tool in diagnosing diabetes.ĭiabetes is a metabolic disorder characterized by high blood sugar levels that causes severe damage to the nerves, heart, eyes, kidneys, and blood vessels. The results of this study are quite reasonable and successful when compared with other studies. The feature selection method improves the prediction time, although it does not affect the accuracy of the four compared classifiers. The results show that the proposed system achieves an accuracy of 99.2%, an AUC of 99.3%, and a prediction time of 0.04825 seconds. The performance of the classifiers is measured concerning accuracy (ACC), precision (PPV), recall (SEN, sensitivity), F1 score (F1), and the area under the receiver-operating-characteristic curve (AUC). It contains 520 instances, including 320 diabetics and 200 control instances. The dataset is the diabetic hospital data in Sylhet, Bangladesh. MLR-RF algorithm is used for feature selection, and XG is used for classification in the proposed system. This study aims to introduce a technique based on a combination of multiple linear regression (MLR), random forest (RF), and XGBoost (XG) to diagnose diabetes from questionnaire data. An automated diabetes detection system assists physicians in the early diagnosis of the disease and reduces complications by providing fast and precise results. Early diagnosis and treatment are vital to prevent or delay complications related to diabetes. Diabetes is one of the most common and serious diseases affecting human health.
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