Early Detection of Osteoporosis and Osteopenia Disease Using Computational Intelligence Techniques

People are now more frequently affected by the disorders of osteoporosis and osteopenia. Food habits and genetics have been recognized as the main contributing factors; hence earlier disease prediction is necessary for the conditions mentioned above. Osteoporosis and osteopenia generally affect older adults and women who have passed menopause to a greater extent. Early identification of osteoporosis and osteopenia is crucial to prevent bone fractures and fragility. Low bone mineral density is the cause of bone fragility and fracture. Clinical information such as bone mineral density and radiographic images are used to perform a predictive study of the disorders mentioned above. Bone mineral density values are beneficial when determining T-Score and Z-Score values, which can be used to categorize osteoporosis and osteopenia. Several computational intelligence strategies forecast and categorize osteoporosis and osteopenia disorders. The various computational methodologies utilized to predict the same have been thoroughly explored in this chapter. Further, the chapter has been concluded with the appropriate metrics.