title: Non-invasive detection of fasting blood glucose level via electrochemical measurement of saliva.
abstract: Machine learning techniques such as logistic regression (LR), support vector machine (SVM) and artificial neural network (ANN) were used to detect fasting blood glucose levels (FBGL) in a mixed population of healthy and diseased individuals in an Indian population. The occurrence of elevated FBGL was estimated in a non-invasive manner from the status of an individual's salivary electrochemical parameters such as pH, redox potential, conductivity and concentration of sodium, potassium and calcium ions. The samples were obtained from 175 randomly selected volunteers comprising half healthy and half diabetic patients. The models were trained using 70 % of the total data, and tested upon the remaining set. For each algorithm, data points were cross-validated by randomly shuffling them three times prior to implementing the model. The performance of the machine learning technique was reported in terms of four statistically significant parameters-accuracy, precision, sensitivity and F1 score. SVM using RBF kernel showed the best performance for classifying high FBGLs with approximately 85 % accuracy, 84 % precision, 85 % sensitivity and 85 % F1 score. This study has been approved by the ethical committee of All India Institute of Medical Sciences, New Delhi, India with the reference number: IEC/NP-278/01-08-2014, RP-29/2014.
authors.affiliation: Center for Biomedical Engineering, Indian Institute of Technology (IIT) Delhi, New Delhi, 110016 Delhi India.
authors.forename: Sarul
authors.initials: S
authors.lastname: Malik
authors.affiliation: Department of Endocrinology and Metabolism, All India Institute of Medical Sciences (AIIMS), New Delhi, 110016 Delhi India.
authors.forename: Rajesh
authors.initials: R
authors.lastname: Khadgawat
authors.affiliation: Center for Biomedical Engineering, Indian Institute of Technology (IIT) Delhi, New Delhi, 110016 Delhi India ; Department of Biomedical Engineering, All India Institute of Medical Sciences (AIIMS), New Delhi, 110016 Delhi India.
authors.forename: Sneh
authors.initials: S
authors.lastname: Anand
authors.affiliation: Department of Chemical Engineering, Indian Institute of Technology (IIT) Delhi, New Delhi, 110016 Delhi India.
authors.forename: Shalini
authors.initials: S
authors.lastname: Gupta
keywords: Artificial neural network; Diabetes; Logistic regression; Machine learning; Saliva; Support vector machine