Detection of Inadequate Growth of Early Childhood and Development of Adult Disease Alert via Embedded IoT Systems Using Cognitive Computing

The objective of the work is to bring the awareness of the childhood growth problems and the corresponding developmental adult diseases like CVD and Type 2 diabetes using IoT-embedded systems. Nowadays, there are a wide range of abnormalities found in the early growth of the child due to malnutrition and enteric infections which increase the risk for the metabolic syndrome. This leads to stunting (poor height growth) and obesity in the young generation. The medical record of the child from the infant stage to the age of 18 years is stored in the cloud database and is compared with the reference records and provides information about growth syndromes and guides to appropriate doctor and the hospital. Cognitive computing approach using support vector data description algorithm is applied in the outlier detection. The odd data which are likely to have stunting is marked using the outlier detection in the cloud dataset and an alert is sent to the parents about the stunting effect on the child. The proposed idea helps in building a young and energetic healthy nation.