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Osteoporosis is a global health concern characterized by reduced bone density and compromised bone quality, resulting in an increased risk of fractures, particularly in postmenopausal women. The assessment of bone mineral density (BMD) plays a pivotal role in diagnosing osteoporosis, as it accounts for approximately 70% of overall bone strength. The World Health Organization (WHO) has endorsed BMD measurement as a reliable method for diagnosing this condition. In Pakistan, the incidence of bone fractures is on the rise, largely attributable to an aging population and a range of contributing factors. Understanding the global and local prevalence of osteoporosis, its impact on morbidity and mortality, and the contributing factors is vital for developing effective preventive and therapeutic strategies. The role of adipokines, including chemerin, vaspin, and omentin-1, in bone metabolism is an emerging area of investigation. These adipokines play diverse roles in physiology, ranging from inflammation and metabolic regulation to cardiovascular health. Understanding their potential impact on bone health is a topic of ongoing research. The intricate relationship between bone density, bone quality, and overall bone strength is central to understanding the diagnosis and management of osteoporosis. Current innovation in machine learning and predictive model can bring revolution in the field of bone health and osteoporosis. Early identification of people with osteoporosis or risk of fracture through machine learning can prevent disability and improve the quality of life. [ABSTRACT FROM AUTHOR]

Osteoporosis, also known as low bone mineral density (BMD), is a serious health concern, especially for women who have gone through menopause in community settings. This condition weakens the bones and increases the risk of fractures. Women who have gone through menopause are more susceptible to osteoporosis due to hormonal changes. Therefore, it is crucial to identify the condition early to start preventive treatments and reduce the risk of fractures. To address the challenges of diagnosing low BMD in postmenopausal women in community settings, this study proposes a method that combines machine learning with the AdaBoostM1 algorithm, which has shown promising results. Data acquisition, data preprocessing, data training, model testing, and model prediction and evaluation are integral phases of the operational dynamics of our model in osteoporosis diagnosis. This approach recommends increasing screening initiatives and educating patients as strategies to improve early detection and management of the disease. The analysis method used achieved an impressive accuracy rate of approximately 88.8% on the dataset it was applied to. The area under the curve was 0.87, the true positive rate was 88%, and the F1 measure was 0.88. By using accurate diagnostic techniques and providing proactive community care, the incidence of osteoporotic fractures can be significantly reduced, thus improving the quality of life for this vulnerable population. [ABSTRACT FROM AUTHOR]