Abstract
Diabetes Mellitus affects adults and children, causing changes in lifestyle. The diabetic affected person count has increased drastically worldwide over the last few years; about 425 million people have diabetes. By 2030, it is predicted that diabetic disorder will be the seventh leading cause of human death. Diabetes mellitus is measured invasively. This method has limitations such as patient’s preparation, piercing of the skin, which causes infection and needs for skilled technicians. In order to avoid the limitations of invasive methods, vibrations from the pancreas are acquired using a smartphone accelerometer sensor and detecting the value of diabetes. The human body has a unique energy signature for every organ, which leads to vibrations with different frequencies. The frequency of the vibration signal from the pancreas is proportional to insulin secretion and dynamics. The signals obtained from the accelerometer sensor are trained and analyzed with the Levenberg–Marquardt algorithm for obtaining the relation between the excess insulin secretion and clinical value of the diabetic level of the person. The accelerometer signals and clinical values are modeled with Regression analysis for the diabetic and non-diabetic persons. The results show the correlation between the fluid dynamics of insulin and clinical value at about 95% in prediction.
Published Version
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