Machine learning‐based prediction of osteoporosis in postmenopausal women with clinical examined features: A quantitative clinical study

Abstract

Osteoporosis is a skeletal disease that is commonly seen in older people but often neglected due to its silent nature. To overcome the issue of osteoporosis in men and women, we proposed an advanced prediction model with the help of machine learning techniques which can help to identify the potential occurrence of this bone disease by its advanced screening tools. To achieve more reliable and accurate results, various machine‐learning techniques were applied to the presented data sets. Moreover, we also compared the performance of our results with other existing algorithms to solely focus on the advanced features of the proposed methodology. The two data sets, the clinical tests of patients in Taiwan and medical reports of postmenopausal women in Korea through Korean Health and Nutrition Examination Surveys (2010–2011) were considered in this study. To predict bone disorders, we utilized the data about females and developed a system using artificial neural networks, support vector machines, and K‐nearest neighbor. To compare the performance of the model Area under the Receiver Operating Characteristic Curve and other evaluation metrics were compared. The achieved results from all the algorithms and compared them with Osteoporosis Self‐Assessment Tool for Asians and the results were noticeably better and more reliable than existing systems due to the involvement of ML. Using machine learning techniques to predict these types of diseases is better because physicians and patients can take early action to prevent the consequences in advance.

Segmentation and classification of skin lesions using hybrid deep learning method in the Internet of Medical Things

“…Dermoscopy is used to collect photographs of the skin, while a biopsy and a microscope are required to obtain images of other medical structures. 14 15,16 and other deep learning algorithms [17][18][19][20][21][22] have shown great progress in various skin cancer classifications, with excellent accuracy and robustness. Extraction of discriminative features from photos of skin lesions has been used to obtain outstanding classification results using DenseNet, 23 InceptionNet, 24 and ResNet.…”

“…The difficulty of differentiating malignant from benign skin lesions has prompted the development of several cutting‐edge techniques. Convolutional Neural Networks (CNNs) 15,16 and other deep learning algorithms 17–22 have shown great progress in various skin cancer classifications, with excellent accuracy and robustness. Extraction of discriminative features from photos of skin lesions has been used to obtain outstanding classification results using DenseNet, 23 InceptionNet, 24 and ResNet 25 .…”

Predictive Modeling of Osteoporosis Risk Factors using XGBoost and Bagging Ensemble Technique

“…The global increase in the elderly population has made osteoporosis a major public health issue, leading to high levels of illness, death, and healthcare expenses [2]. Early identification of those susceptible to osteoporosis is essential for initiating preventative actions and lessening the impact of osteoporotic fractures [3].…”

“…For instance, a study by Kainat et al investigated the use of ANN, SVM, KNN algorithms to identify significant risk factors for osteoporotic fractures in postmenopausal women. Their findings revealed that age, bone mineral density, and previous fracture history were among the most influential predictors of fracture risk [3]. Similarly, Xuangao Wu and Sunmin Park employed ML approach to develop predictive models for osteoporosis diagnosis using clinical data.…”