Development of Artificial Neural Network for body composition Analysis

Artificial Neural networks (ANN) are finding many uses in the medical diagnosis applications. Different diseases such as acquired immuno deficiency syndrome (AIDS), malnutrition, cardiovascular diseases, osteoporosis are related to body composition topologies such as fat mass (FM), fat free mass (FFM), total body water (TBW), bone mineral contents (BMC) and bone mineral density (BMD). Due to heterogeneous complexity of medical data classification and analysis needs Artificial Intelligence (AI) based technique to manipulate data. Many e-health system especially ANN uses AI methods to improve diagnostic process. Currently many body composition measurement systems in their applications elderly. Hence bioelectric impedance analysis (BIA) technique is used which is non-invasive, easy, fast and inexpensive. In biological structure, application of low level alternation of current produces impedance to spread current. These impedance and phage angle is measured using different electrodes to calculate resistive and reactive components of the body. These components along with other independent variables such as age, height, weight, etc are used to calculate BMC and BMD respectively. ANN provides mathematical equations which are used to calculate BMC and BMD, which are useful body composition parameters for the detection of osteoporosis.