Design and Implementation of Non-Invasive Technique Blood Glucose and Cholesterol Detection Using Machine Learning

In the present world, diabetes and cholesterol is one of the common diseases. The glucose and cholesterol level should be continuously monitored for everyone. This is implemented by an invasive method which involves finger pricking with the lancet and it becomes painful. If this become a daily routine it causes blood related infections and trauma. However, the proposed technique provides a method of determining blood glucose and cholesterol levels that does not require physical contact or blood samples. The technique is attained by using NIR sensor and Raspberry pi. When the patient’s finger is kept on the NIR sensor, which is used to assess cholesterol and glucose measurement. Numerous cardiovascular disorders, such as heart attack and stroke, are caused by the minimum and maximum levels of cholesterol in the blood. Based on the NIR sensor, values are provided to the Raspberry pi. Using an algorithm loaded into the Raspberry pi, the values of glucose and cholesterol are calculated. The application of machine learning techniques can enhance the precision of predictions for cholesterol and glucose.