DiaBeaTiT:

Blood Sugar prediction using Saliva

Defining the Problem:

Diabetes is a long-term condition that causes high blood sugar levels. Treatment requires:

1. Tracking and monitoring of blood sugar level at uniform and frequent intervals.

2. Doing appropriate physical exercise, undertaking appropriate diet, controlling stress levels.

3. Taking insulin doses regularly.

Inconveniences faced by Patients and Doctors in the treatment procedure:

1. Blood Sugar tracking is an invasive process, it requires person’s blood to calculate glucose level. There is a need for changing the blood sample collecting plate for every test which makes the system even more expensive.

2. The discomfort of pain and risk of infection can sometimes be a source of great stress and concern.

3. No facility to check glucose level in remote areas without a proper setup.

Approach to solve the Problem:

Non Invasive Blood Glucose level Calculation using saliva

Step 1: Establish theoretically, the effect of variation of blood glucose level on parameters of chemical, electrical or optical parameters of saliva and urine.

Step 2: Generate the data set of the variations in selected parameters from Step 1 and blood glucose level.

Step 3: Analyse the data of Step 2 to find correlation.

Construction of the Device

Part A: Construction of the Spectrometer

Near IR range LEDs (940nm) and photodiodes are placed opposite to each other considering an optical rotation of 180 degrees. The peak absorption of Glucose is observed at 940nm in it's spectrum.

These LED's are connected to Arduino NANO which is transferring data to Raspberry Pi zero .

A python script running on raspberry pi zero uploads these values to firebase.

Callibration

The intensity of emitter LEDs is kept constant.

With variation in sample solution, the intensity of radiation at the other end of photodiode varies.

200 samples of known glucose concentration are placed in the device and attenuation metric is recorded.

According to Beer's Law, concentration and attanuation vary in polynomial fashion hence, polynomial regression analysis is done to map attenuation values to concentration.

The obtained array of Cofficients can be given as: Pmc = [0.00000000e+00, 1.03820866e-03, 2.85167552e-06, -1.52347882e-09] Cs(A) = Polynomial(A,Pmc)

Range of A: 350 to 1023

Accuracy Observed: 92%

Part B: Glucose Level Prediction

Samples of saliva and blood of 52 people were collected and data was analysed.

Polynomial Regression turned out to be most computationally economics and accurate tool considering this to be a small sample data.

Accuracy of 91% was achieved The Coefficient Matrix for this Polynomial was found to be: Psa = [ 0.0 , 61.58593563, 14.3749855 ]

Blood Glucose Concentration (Salivary Glucose Concentration) = Polynomial(Sal,Psa)

Hence, overall mathematical model is presented as:

Pmc = [0.00000000e+00, 1.03820866e-03, 2.85167552e-06, -1.52347882e-09] Psa = [ 0.0 , 61.58593563, 14.3749855 ] BGL(A) = Polynomial(Polynomial(A,Pmc),Psa)

where A is the attenuation metric or the Microcontroller Value The Overall Accuracy for the device was found to be 82%

Part C: Cloud Interface

Cloud Tool : Firebase

The attenuation values are sent to firebase and an app interface is used to view the Blood Glucose Level. Alternatively, these Mathematical models were also be incorporated in Raspberry Pi so that it is able to display immediate results.