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.

Keywords: classification; machine learning; osteoporosis; osteoporotic fractures; prediction.

Figure 1

Flowchart of Data set 1 and preprocessing of patients HRT (hormone replacement therapy) BP (blood pressure).

Figure 2

Flowchart of Data set 2 of postmenopausal women.

Figure 3

Conceptual flowchart of the proposed methodology.