Clustering of Various Parameters to Catalog Human Bone Disorders Through Soft Computing Simulation

Every minute nearly 20 fractures occur due to bone disorders in the world. People around the world could not able to differentiate the difference between bone disorders. This chapter is a novel approach toward differentiation of different bone disorders like osteoporosis and osteopenia with influences several parameters. Accordingly, five different parameters such as Calcium, Phosphate, Vitamin D3, Parathyroid hormone (PTH) level, and calcitonin level are considered for the study to categorize the bone disorders. The present approach is an attempt to combine the clinical data measured from each patient and their respective bone mineral density value for the better classification. This is a unique study to provide combined information of both clinical and image processing studies. For this purpose, the above-mentioned parameters and bone density values were observed from ten different patients. All these data were used as an input for soft computing using MATLAB for further processing the data. Initially, unsupervised mapping classifier is adopted to classify bone disorder, for which the clinical parameters are compared with bone density value using k-means clustering algorithm. The prime idea behind using of k-means technique is that the feasibility to classify the inputs based on the distance between the input seeds. With reference to the perpendicular distance between the seed inputs, the bone disorders have been cataloged. The repeated iterations lead to best clustering results.