VitaDNet: A Deep Learning-Based Approach for Vitamin-D Deficiency Prediction

Abstract

Vitamin D (VD) deficiency is a very common disease among elderly people. The lack of VD causes various diseases related to skin, eyes and throat. The previous epidemiological studies tried to predict the vitamin B6 and VD levels from the blood samples. Since this is laborious and time-taking, it is very difficult for the homely people to work on it. There is a strong requirement for the noninvasive method as there is a necessity to detect the deficiency at the early stage. Certain crucial parameters that could be used for analysis are based on the intake of anthropogenic parameters along with the commonly known body vitals. These parameters include the body mass index (BMI), waist circumference (WC), waist-to-height ratio (WHR) and body roundness index (BRI). The dataset used for the prediction of VD has been collected from 501 patients in the age of 40–75 years old. The prediction of VD levels in the body has various complications, like the sex, previous health records, inherent health conditions and body pathology. To consolidate all those parameters and to analyse, a robust model is required to associate the parameters which are used to predict the deficit of VD. A binary set of gated recurrent units (GRUs) are used along with the auto-encoders. The feature extraction and selection module in the network are composed of two different patch-based networks which makes the three-stage network robust. Despite these difficulties, the model is robust enough to predict the levels of VD in the body based on the anthropogenic parameters. To support this network, a sub-VitaDNet module is proposed based on the food taken. Through this network, the food taken is continuously observed and the levels of VD are predicted. Hence, the authors believe that the model is robust enough to predict the VD levels in the body.