Development of Machine Learning Models for Prediction of Osteoporosis from Clinical Health Examination Data

Abstract

Osteoporosis is treatable but often overlooked in clinical practice. We aimed to construct prediction models with machine learning algorithms to serve as screening tools for osteoporosis in adults over fifty years old. Additionally, we also compared the performance of newly developed models with traditional prediction models. Data were acquired from community-dwelling participants enrolled in health checkup programs at a medical center in Taiwan. A total of 3053 men and 2929 women were included. Models were constructed for men and women separately with artificial neural network (ANN), support vector machine (SVM), random forest (RF), k-nearest neighbor (KNN), and logistic regression (LoR) to predict the presence of osteoporosis. Area under receiver operating characteristic curve (AUROC) was used to compare the performance of the models. We achieved AUROC of 0.837, 0.840, 0.843, 0.821, 0.827 in men, and 0.781, 0.807, 0.811, 0.767, 0.772 in women, for ANN, SVM, RF, KNN, and LoR models, respectively. The ANN, SVM, RF, and LoR models in men, and the ANN, SVM, and RF models in women performed significantly better than the traditional Osteoporosis Self-Assessment Tool for Asians (OSTA) model. We have demonstrated that machine learning algorithms improve the performance of screening for osteoporosis. By incorporating the models in clinical practice, patients could potentially benefit from earlier diagnosis and treatment of osteoporosis.

Keywords: early detection; machine learning; osteoporosis; prediction model; screening tool.

Figure 1

Flowchart of data inclusion and preprocessing. DXA: dual-energy X-ray absorptiometry; HbA1c: Hemoglobin A1c; ALT: alanine transaminase; ALK-P: alkaline phosphatase; TG: triglyceride; HDL-C: high-density lipoprotein cholesterol; TSH: thyroid-stimulating hormone.

Figure 2

(A)The ROC curves of different machine learning models and the OSTA model for prediction of osteoporosis in men. ANN: Artificial neural network; SVM: Support vector machine; RF: Random Forest; KNN: K-nearest neighbors; LoR: Logistic regression; OSTA: Osteoporosis Self-assessment Tool for Asian; ROC curve: Receiver operating characteristic curve. (B) The ROC curves of different machine learning models and the OSTA model for prediction of osteoporosis in women.

Figure 2

(A)The ROC curves of different machine learning models and the OSTA model for prediction of osteoporosis in men. ANN: Artificial neural network; SVM: Support vector machine; RF: Random Forest; KNN: K-nearest neighbors; LoR: Logistic regression; OSTA: Osteoporosis Self-assessment Tool for Asian; ROC curve: Receiver operating characteristic curve. (B) The ROC curves of different machine learning models and the OSTA model for prediction of osteoporosis in women.