A Fuzzy Rule-Based System for Classification of Diabetes

Diabetes is a fatal disease that currently has no treatment. However, early diagnosis of diabetes aids patients to start timely treatment and thus reduces or eliminates the risk of severe complications. The prevalence of diabetes has been rising rapidly worldwide. Several methods have been introduced to diagnose diabetes at an early stage, however, most of these methods lack interpretability, due to which the diagnostic process cannot be explained. In this paper, fuzzy logic has been employed to develop an interpretable model and to perform an early diagnosis of diabetes. Fuzzy logic has been combined with the cosine amplitude method, and two fuzzy classifiers have been constructed. Afterward, fuzzy rules have been designed based on these classifiers. Lastly, a publicly available diabetes dataset has been used to evaluate the performance of the proposed fuzzy rule-based model. The results show that the proposed model outperforms existing techniques by achieving an accuracy of 96.47%. The proposed model has demonstrated great prediction accuracy, suggesting that it can be utilized in the healthcare sector for the accurate diagnose of diabetes.

Biomarker Glycated Hemoglobin (HbA1c) in Diabetes Mellitus Type II

Early diagnosis of diabetes is clinically important in reducing health complications worldwide. In this respect HbA1c has become an accurate biomarker for the diagnosis of Diabetes Mellitus (DM) and its complication [1]. In the present study HbA1c measured in subject of age <20,21-30,31-40 yrs and the level found to show high risk for DM in youngsters. Hence counselling at least once a month is warranted. To be most effective to reduce or prevent the prevalence in youngsters the importance of controlling HbA1c and keeping it at low level can be achieved by including in the curriculum right from school ageing. It will reduce the financial burden on state and central government authorities. Key words: HbA1c, Diabetes Mellitus 2.

Next generation plasma proteome profiling to monitor health and disease

The need for precision medicine approaches to monitor health and disease makes it important to develop sensitive and accurate assays for proteome profiles in blood. Here, we describe an approach for plasma profiling based on proximity extension assay combined with next generation sequencing. First, we analyze the variability of plasma profiles between and within healthy individuals in a longitudinal wellness study, including the influence of genetic variations on plasma levels. Second, we follow patients newly diagnosed with type 2 diabetes before and during therapeutic intervention using plasma proteome profiling. The studies show that healthy individuals have a unique and stable proteome profile and indicate that a panel of proteins could potentially be used for early diagnosis of diabetes, including stratification of patients with regards to response to metformin treatment. Although validation in larger cohorts is needed, the analysis demonstrates the usefulness of comprehensive plasma profiling for precision medicine efforts.

Predicting Diabetes Mellitus With Machine Learning Techniques Using Multi-Criteria Decision Making

Diabetes has become one of the common health issues in people of all age groups. The disease is responsible for many difficulties in lifestyle and is represented by imbalance in hyperglycemia. If kept untreated, diabetes can raise the chance of heart attack, diabetic nephropathy, and other disorders. Early diagnosis of diabetes helps to maintain a healthy lifestyle. Machine learning is a capability of machine to learn from past pattern and occurrences and converge with experience to optimise and give decision. In the current research, the authors have employed machine learning techniques and used multi-criteria decision-making approach in Pima Indian diabetes dataset. To classify the patients, they examined several different supervised and unsupervised predictive models. After detailed analysis, it has been observed that the supervised learning algorithms outweigh the unsupervised algorithms due to the output class being a nominal classified domain.

Data Mining Techniques for Early Diagnosis of Diabetes: A Comparative Study

Diabetes is a life-long condition that is well-known in the 21st century. Once known as a disease of the West, the rise of diabetes has been fed by a nutrition shift, rapid urbanization and increasingly sedentary lifestyles. In late 2019, a new public health concern was emerging (COVID-19), with a particular hazard concerning people living with diabetes. Medical institutes have been collecting data for years. We expect to achieve predictions for pathological complications, which hopefully will prevent the loss of lives and improve the quality of life using data mining processes. This work proposes a comparative study of data mining techniques for early diagnosis of diabetes. We use a publicly accessible data set containing 520 instances, each with 17 attributes. Naive Bayes, Neural Network, AdaBoost, k-Nearest Neighbors, Random Forest and Support Vector Machine methods have been tested. The results suggest that Neural Networks should be used for diabetes prediction. The proposed model presents an AUC of 98.3% and 98.1% accuracy, an F1-Score, Precision and Sensitivity of 98.4% and a Specificity of 97.5%.