An intelligent bio-inspired system for detection and prediction of osteoporosis

Abstract

Osteoporosis, a bone-related disorder that causes bone loss and other medical complications. Osteoporosis diagnosis is a lengthy process and requires a number of procedures. This disease is found to be difficult to detect in remote areas due to lack of high-tech equipment. Vibroacoustic response of bones may recognize the quality of the bone. The signal (Mel-frequency cepstral coefficients) from the reflex hammer which is used in an artificial neural network provides more than 70% accuracy. To avoid the above-mentioned problems and to improve the process of identifying this disease and to improve the efficiency and accuracy of the identification process, we propose this method which helps to determine the chance of this disease in patients by analyzing a classifier using an artificial immune system that classifies the affected and the unaffected using the history of the patient The use of the artificial immune system is 94 per cent effective. The classifier we used therefore defines and predicts the probability of the disease occurring.