Use of Artificial Intelligence for Identification of Increased Risk of Osteoporosis Development in Postmenopausal Women
This paper presents an Artificial Nerual Network (ANN) for identification of postmenopausal women who are at high risk for developing osteopathy. While 800 patients took part in the study, 180 were used for network training. The following parameters were used: T-score (from −2,5 to −4), Age, Blood calcium level (<1,9 mmol/L), Blood vitamin D level (<20 ng/ml), Hip fracture, Spine fracture, Joint fracture, Glucocorticoids use, Smoking status, and BMI. The network has 10 input parameters and 1 output parameter. For the final architecture of expert system, a neural network with 20 neurons in hidden layer was chosen based on the training results. The signal from each neuron from hidden layer is directed to neuron in output layer, where this neuron processes the signal and gives desired output of the network. The sensitivity was 97,5%, specificity 70%, and accuracy 94,44%.