DIABETES MELLITUS: ENQUIRY INTO ITS MEDICAL ASPECTS AND BIOENGINEERING OF ITS MONITORING AND REGULATION

Bioactive Compounds and Diabetes Mellitus: Prospects and Future Challenges

Diabetes mellitus is a metabolic condition that influences the endocrine framework. Hyperglycemia and hyperlipidemia are two of the most widely recognized metabolic irregularities in diabetes, just as two of the most well-known reasons for diabetic intricacies. Diabetes mellitus is a persistent illness brought about by metabolic irregularities in hyperglycemic pancreatic cells. Hyperglycemia can be brought about by an absence of insulin-producing beta cells in the pancreas (Type 1 diabetes mellitus) or inadequate insulin creation that does not work effectively (Type 2 diabetes mellitus). Present diabetes medication is directed toward directing blood glucose levels in the systemic circulation to the typical levels. Numerous advanced prescription medicines have many negative results that can bring about unexpected severe issues during treatment of the bioactive compound from a different source that is beneficially affected by controlling, adjusting metabolic pathways or cycles. Moreover, a few new bioactive medications disengaged from plants have shown antidiabetic action with more noteworthy adequacy than the oral hypoglycemic agent that specialists have utilized in clinical treatment lately. Since bioactive mixtures are collected from familiar sources, they have a great activity in controlling diabetes mellitus. This study discusses bioactive compounds and their activity to manage diabetes mellitus and their prospects. Though bioactive compound has many health beneficial properties, adequate clinical studies still need to gain large acknowledge that they are effective in the management of diabetes mellitus.

THE EFFECT OF DIABETES ON CARDIOVASCULAR SYSTEM

Diabetes mellitus (DM) is a critical and long-term disorder due to the insufficient production of insulin by the pancreas or ineffective use of insulin by the body. Importantly, cardiovascular disease (CVD) has long been thought to be linked with diabetes. Despite more diabetic individuals surviving from better medications and treatments, there has been significant rise in the morbidity and mortality from CVD. Indeed, the classification of DM based on the electrocardiogram signals of the heart will be an advantageous system. Further, computer-aided classification of DM with integrated algorithms may enhance the execution of the system. In this paper, we have reviewed various studies using heart rate variability signals for automated classification of diabetes. Furthermore, the different techniques used to extract the features and the efficiency of the classification systems are discussed.

A NEW METHOD FOR AUTOMATED DETECTION OF DIABETES FROM HEART RATE SIGNAL

Diabetes Mellitus (DM) is a chronic disease and it is characterized based on the increase in the sugar level in the blood. The other diseases such as the cardiomyopathy, neuropathy and retinopathy may occur due to the DM pathology. The RR-time series or heart rate (HR) signal quantifies the beat-to-beat variations in the electrocardiogram (ECG) and it has been widely used for the detection of various cardiac diseases. Detection of DM based on the features of HR signal is a challenging problem. This paper copes with a new method for the detection of Diabetes Mellitus (DM) based on the features extracted from the HR signal. The Singular Spectrum Analysis (SSA) of HR signal and the Kernel Sparse Representation Classifier (KSRC) are the mathematical foundations used to achieve the detection. SSA is used to decompose the HR signal into sub-signals, and diagnostic features such as the maximum value of each sub-signal and eigenvalues are evaluated. Then, the KSRC uses the proposed diagnostic features as inputs for detecting diabetes. The experimental results reveal that the proposal attains the accuracy, sensitivity, and specificity values of 92.18%, 93.75% and 90.62%, respectively, employing the KSRC and the hold-out cross-validation approach. The method is compared with existing approaches for detecting diabetes from HR signal.