• Journal of Korean Academy of Nursing
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• v.51 no.3
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• pp.280-293
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• 2021

Purpose: This study aimed to identify the risk factors for diabetic foot ulceration (DFU) to develop and evaluate the performance of a DFU prediction model and nomogram among people with diabetes mellitus (DM). Methods: This unmatched case-control study was conducted with 379 adult patients (118 patients with DM and 261 controls) from four general hospitals in South Korea. Data were collected through a structured questionnaire, foot examination, and review of patients' electronic health records. Multiple logistic regression analysis was performed to build the DFU prediction model and nomogram. Further, their performance was analyzed using the Lemeshow-Hosmer test, concordance statistic (C-statistic), and sensitivity/specificity analyses in training and test samples. Results: The prediction model was based on risk factors including previous foot ulcer or amputation, peripheral vascular disease, peripheral neuropathy, current smoking, and chronic kidney disease. The calibration of the DFU nomogram was appropriate (χ² = 5.85, p = .321). The C-statistic of the DFU nomogram was .95 (95% confidence interval .93~.97) for both the training and test samples. For clinical usefulness, the sensitivity and specificity obtained were 88.5% and 85.7%, respectively at 110 points in the training sample. The performance of the nomogram was better in male patients or those having DM for more than 10 years. Conclusion: The nomogram of the DFU prediction model shows good performance, and is thereby recommended for monitoring the risk of DFU and preventing the occurrence of DFU in people with DM.

• International Journal of Computer Science & Network Security
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• v.21 no.9
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• pp.19-30
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• 2021

Diabetes Mellitus (DM) is one of common chronic diseases leading to severe health complications that may cause death. The disease influences individuals, community, and the government due to the continuous monitoring, lifelong commitment, and the cost of treatment. The World Health Organization (WHO) considers Saudi Arabia as one of the top 10 countries in diabetes prevalence across the world. Since most of the medical services are provided by the government, the cost of the treatment in terms of hospitals and clinical visits and lab tests represents a real burden due to the large scale of the disease. The ability to predict the diabetic status of a patient without the laboratory tests by performing screening based on some personal features can lessen the health and economic burden caused by diabetes alone. The goal of this paper is to investigate the prediction of diabetic and prediabetic patients by considering factors other than the laboratory tests, as required by physicians in general. With the data obtained from local hospitals, medical records were processed to obtain a dataset that classified patients into three classes: diabetic, prediabetic, and non-diabetic. After applying three machine learning algorithms, we established good performance for accuracy, precision, and recall of the models on the dataset. Further analysis was performed on the data to identify important non-laboratory variables related to the patients for diabetes classification. The importance of five variables (gender, physical activity level, hypertension, BMI, and age) from the person's basic health data were investigated to find their contribution to the state of a patient being diabetic, prediabetic or normal. Our analysis presented great agreement with the risk factors of diabetes and prediabetes stated by the American Diabetes Association (ADA) and other health institutions worldwide. We conclude that by performing class-specific analysis of the disease, important factors specific to Saudi population can be identified, whose management can result in controlling the disease. We also provide some recommendations learnt from this research.

• Journal of Biomedical Engineering Research
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• v.28 no.3
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• pp.355-362
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• 2007

Diabetes mellitus can cause devastating complications, which often result in disability and death, and diabetic nephropathy is a leading cause of death in people with diabetes. In this study, we tried to predict the onset of diabetic nephropathy from an irregular and unbalanced diabetic dataset. We collected clinical data from 292 patients with type 2 diabetes and performed preprocessing to extract 184 features to resolve the irregularity of the dataset. We compared several feature selection methods, such as ReliefF and sensitivity analysis, to remove redundant features and improve the classification performance. We also compared learning methods with support vector machine, such as equal cost learning and cost-sensitive learning to tackle the unbalanced problem in the dataset. The best classifier with the 39 selected features gave 0.969 of the area under the curve by receiver operation characteristics analysis, which represents that our method can predict diabetic nephropathy with high generalization performance from an irregular and unbalanced dataset, and physicians can benefit from it for predicting diabetic nephropathy.

• Journal of Physiology & Pathology in Korean Medicine
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• v.36 no.3
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• pp.94-99
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• 2022

The discovery of biomarkers related to pattern identification (PI), the core diagnostic theory of Korean medicine (KM), is one of the methods that can provide objective and reliable evidence by applying PI to clinical practice. In this study, 40 diabetic patients and 41 healthy control subjects recruited from the Korean medicine clinic were examined to determine the human electrical response related to the deficiency pattern, a representative pattern of diabetes. Qi-Blood-Yin-Yang deficiency pattern scores, which are representative deficiency patterns for diabetes mellitus, were obtained through a questionnaire with verified reliability and validity, and the human electrical response was measured non-invasively using a bioimpedance meter. In ANCOVA analysis using gender as a covariate, the 5 kHz frequency resistance and 5-250 kHz frequency reactance were significantly lower in the diabetic group than in non-diabetic control group. In addition, the multiple regression analysis showed a positive correlation (R2=0.11~0.19) between the Yang deficiency pattern score and resistance value for the diabetic group; the correlation was higher at higher frequencies of 50kHz (R2=0.18) and 250kHz (R2=0.19) compared to 5kHz(R2=0.11). In contrast, there was no such significant association in the control group. It implies that bioimpedance resistance measured at finite frequencies may be useful in predicting Yang deficiency, which is closely related to diabetic complications by reflecting the decrease in body water content and metabolism. In the future, large-scale planned clinical studies will be needed to identify biomarkers associated with different types of PI in diabetes.

• Journal of Physiology & Pathology in Korean Medicine
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• v.31 no.6
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• pp.313-327
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• 2017

The purpose of this study is to predict the effects of macroscopic and integrative therapies by finding active ingredients, potential targets of Astragalus membranaceus (Am) and Cornus officinalis (Co) for diabetic nephropathy. We have constructed network pharmacology-based systematic and network methodology by system biology, chemical structure, chemogenomics. We found several active ingredients of Astragalus membranaceus (Am) and Cornus officinalis (Co) that were speculated to bind to specific receptors which had been known to have a role in the progression of diabetic nephropathy. Four components of Am and eleven components of Co could bind to iNOS; two ingredients of Am and six ingredients of Co could docking to cGB-PDE; one component of Am and nine components of Co could bind to ACE; three ingredients of Co with neprilysin; three components of Co with ET-1 receptor; four ingredients of Am and fourteen ingredients of Co with mineralocorticoid receptor; one component of Am and seven components of Co with interstitial collagenase; one ingredient of Am and ten ingredients of Co with membrane primary amine oxidase; one component of Am and four components of Co with JAK2; two ingredients of Am and one ingredient of Co with MAPK 12; one component of Am and five components of Co could docking to TGF-beta receptor type-1. From this work we could speculate that the possible mechanisms of Am and Co for diabetic nephropathy are anti-inflammatory, antioxidant and antihypertensive effects.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.9
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• pp.2904-2926
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• 2022

Currently, diabetes is the most common chronic disease in the world, affecting 23.7% of the population in the Kingdom of Saudi Arabia. Diabetes may be the cause of lower-limb amputations, kidney failure and blindness among adults. Therefore, diagnosing the disease in its early stages is essential in order to save human lives. With the revolution in technology, Artificial Intelligence (AI) could play a central role in the early prediction of diabetes by employing Machine Learning (ML) technology. In this paper, we developed a diagnosis system using machine learning models for the detection of type 2 diabetes among adults, through the adoption of two different diabetes datasets: one for training and the other for the testing, to analyze and enhance the prediction accuracy. This work offers an enhanced classification accuracy as a result of employing several pre-processing methods before applying the ML models. According to the obtained results, the implemented Random Forest (RF) classifier offers the best classification accuracy with a classification score of 98.95%.

• International Journal of Computer Science & Network Security
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• v.23 no.12
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• pp.204-212
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• 2023

Diabetes is a condition that can be brought on by a variety of different factors, some of which include, but are not limited to, the following: age, a lack of physical activity, a sedentary lifestyle, a family history of diabetes, high blood pressure, depression and stress, inappropriate eating habits, and so on. Diabetes is a disorder that can be brought on by a number of different factors. A chronic disorder that may lead to a wide range of complications. Diabetes mellitus is synonymous with diabetes. There is a correlation between diabetes and an increased chance of having a variety of various ailments, some of which include, but are not limited to, cardiovascular disease, nerve damage, and eye difficulties. There are a number of illnesses that are connected to kidney dysfunction, including stroke. According to the figures provided by the International Diabetes Federation, there are more than 382 million people all over the world who are afflicted with diabetes. This number will have risen during the years in order to reach 592 million by the year 2035. There are a substantial number of people who become victims on a regular basis, and a significant percentage of those people are uninformed of whether or not they have it. The individuals who are most adversely impacted by it are those who are between the ages of 25 and 74 years old. This paper reviews about various machine learning techniques used to detect diabetes mellitus.

• Herbal Formula Science
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• v.32 no.1
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• pp.11-28
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• 2024

Objectives : GunRyeong-Tang(GRT) is a traditional herbal prescription that combines Oryeongsan and Sagunja-tang. This study employed network analysis methods on the components of GRT and target genes related to diabetes complications to predict the improvement effects of GRT on diabetes complications. Methods : The collection of active compounds of GRT and related target genes involved the utilization of public databases and the PubChem database. We selected diabetes complication-related genes using GeneCards and confirmed their correlation through comparative analysis with the target genes of GRT. We constructed a network using Cytoscape 3.9.1 and conducted topological analysis. To predict the mechanism, we performed functional enrichment analysis based on Gene Ontology (GO) biological processes and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Results : Through network analysis, 234 active compounds and 1361 related genes were collected from GRT. A total of 9,136 genes related to diabetes complications were collected, and 1,039 target genes overlapping with the components of GRT were identified. The core genes of this network were TP53, INS, AKT1, ALB, and EGFR. In addition, GRT significantly reduced the H9c2 cell size and the expression of myocardial hypertrophy biomarkers (ANP, BNP), which were increased by high glucose (HG). Conclusions : Through this study, we were able to predict the activity and mechanism of action of GRT on diabetes and diabetic complications, and confirmed the potential of GRT as a treatment for diabetes complications through the effect of GRT on improving myocardial hypertrophy for diabetic cardiomyopathy.

• Journal of Lipid and Atherosclerosis
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• v.7 no.2
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• pp.110-121
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• 2018

Objective: We developed a new equation for predicting coronary heart disease (CHD) risk in Korean diabetic patients using a hospital-based cohort and compared it with a UK Prospective Diabetes Study (UKPDS) risk engine. Methods: By considering patients with type 2 diabetes aged ${\geq}30years$ visiting the diabetic center in Boramae hospital in 2006, we developed a multivariable equation for predicting CHD events using the Cox proportional hazard model. Those with CHD were excluded. The predictability of CHD events over 6 years was evaluated using area under the receiver operating characteristic (AUROC) curves, which were compared using the DeLong test. Results: A total of 732 participants (304 males and 428 females; mean age, $60{\pm}10years$; mean duration of diabetes, $10{\pm}7years$) were followed up for 76 months (range, 1-99 month). During the study period, 48 patients (6.6%) experienced CHD events. The AUROC of the proposed equation for predicting 6-year CHD events was 0.721 (95% confidence interval [CI], 0.641-0.800), which is significantly larger than that of the UKPDS risk engine (0.578; 95% CI, 0.482-0.675; p from DeLong test=0.001). Among the subjects with <5% of risk based on the proposed equation, 30.6% (121 out of 396) were classified as ${\geq}10%$ of risk based on the UKPDS risk engine, and their event rate was only 3.3% over 6 years. Conclusion: The UKPDS risk engine overestimated CHD risk in type 2 diabetic patients in this cohort, and the proposed equation has superior predictability for CHD risk compared to the UKPDS risk engine.

• Journal of Digital Convergence
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• v.18 no.8
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• pp.231-242
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• 2020

The thesis presents a system that continuously collects the human body's physiological vital information at rest with sensors and ICT information technology and predicts diabetes using the collected information. it shows the artificial neural network machine learning method and essential basic variable values. The study method analyzed the correlation between heart rate measurements of BCG and ECG sensors in 20 DM- and 15 DM+ subjects. Artificial Neural Network (ANN) machine learning program was used to predictability of diabetes. The input variables are time domain information of HRV, heart rate, heart rate variability, respiration rate, stroke volume, minimum blood pressure, highest blood pressure, age, and sex. ANN machine learning prediction accuracy is 99.53%. Thesis needs continuous research such as diabetic prediction model by BMI information, predicting cardiac dysfunction, and sleep disorder analysis model using ANN machine learning.