Type 2 diabetes diagnosis assisted by machine learning techniques through the analysis of FTIR spectra of saliva

Diabetes is one of the four main non-communicable diseases worldwide. Despite not being a fatal disease, many complications derive from this illness that causes a drastic deterioration in the patient's health. Diabetes is a silent disease that, on many occasions, causes symptoms until the disease is already advanced, and due to the lack of education in health prevention, only a small part of the population undergoes routine laboratory studies. If this disease is detected on time, the quality of life could be improved. However, the simple facts of taking a blood sample, control studies are omitted. Besides, there is a need to sample the patient many times according to its severity and control. In the present work, we provide a novel technique based on the FTIR spectra of saliva samples to diagnose this disease. After analyzing the samples of 1,000 people, we found that it is possible to identify patients with this pathology through artificial neural networks and SVMr reliably. As it is not invasive and does not require reagents or complex processes, the proposed technique could be more agile and cheaper than traditional ones.

Automated summary

Diabetes, a non-lethal disease, can lead to various complications if not diagnosed promptly, highlighting the importance of early detection for improving quality of life.