Predictive Modelling of Osteoporosis using Machine Learning Algorithms

Osteoporosis implies the deterioration of bones or their denser tissue and the subsequent elevated risk of skeletal fractures constitutes a major health problem today. Therefore, early recognition and initiation of treatment should be enacted to enhance the status of patients. This paper aims at presenting an understanding and analysis of the use of these particular methods, and their specificity in the context of osteoporosis modeling using Support Vector Machines. With the help of the large number of features, which are clinical and demographic factors, we are to create a stable model that will allow defining the probability of osteoporosis. The SVM algorithm for dealing with high-dimensional data and classification is used for the investigation of the connection between input characteristics and the advance of the condition. It also makes it easier to determine the most relevant factors that could be used in the creation of a predictive instrument that might help in the early detection of the disease and the subsequent improvement of the available treatment plans. It is with this goal in mind that this research aims at contributing to development of further implementation of machine learning approaches in osteological field, thus laying basis for enhancing diagnostic approaches and patients' management.