A Systematic Review on Osteoporosis Prediction in Postmenopausal Women

Osteoporosis is a prevalent bone disorder that affects millions of postmenopausal women worldwide, leading to increased fracture risk and significant morbidity. Women go through major hormonal changes during this time, most notably a decrease in oestrogen production, which is essential for preserving bone health. Osteoporosis is characterized by the thinning and degradation of bone tissue, which increases fragility and fracture risk. Even though menopause is a normal and unavoidable phase, some things can make postmenopausal osteoporosis riskier. Understanding the risk factors connected with osteoporosis during post menopause is essential for the implementation of suitable preventive interventions and ensuring optimal bone health in this susceptible population. Key topics discussed in this literature review include the impact of hormonal changes during menopause on bone health, the importance of dietary changes, exercise, pharmaceutical treatments, and lifestyle changes in preventing osteoporosis in postmenopausal women. This literature review aims to provide an overview of the current evidence on osteoporosis prediction strategies during the postmenopausal period. The primary objective is to identify effective strategies for reducing the risk of osteoporotic fractures and promoting bone health in this population. The review discusses the different types of predictive models utilized in the literature, such as machine learning (ML) algorithms, deep learning (DL) algorithms and data mining approaches. It examines the strengths, limitations, and validation processes associated with each approach, as well as their predictive accuracy and clinical utility. The review encompasses a comprehensive analysis of published studies, research articles, and clinical trials focusing on osteoporosis prevention interventions specifically targeting postmenopausal women.