A comparison of machine learning algorithms for osteoporosis prediction

Osteoporosis is a disease in which bone mass is reduced and the bones grow weaker as a result of calcium deficiency. With today's technology, diagnosing osteoporosis necessitates tests, as well as the use of drugs. The machine learning algorithms, on the other hand, can assist in the detection of osteoporosis diseases in the human body. Hence, the present study conducts comparisons among the five distinct machine learning approaches. The performance comparisons were made using three different measures and applied to three Osteoporosis datasets. Based on experimental results, the recurrent neural network (RNN) outperforms other machine learning algorithms namely artificial neural networks (ANNs), support vector machines (SVM), and decision trees (DT).