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.

Predictors for success in smoking cessation have been studied, but a prediction model capable of providing a success rate for each patient attempting to quit smoking is still lacking. The aim of this study is to develop prediction models using machine learning algorithms to predict the outcome of smoking cessation. Data was acquired from patients underwent smoking cessation program at one medical center in Northern Taiwan. A total of 4875 enrollments fulfilled our inclusion criteria. Models with artificial neural network (ANN), support vector machine (SVM), random forest (RF), logistic regression (LoR), k-nearest neighbor (KNN), classification and regression tree (CART), and naïve Bayes (NB) were trained to predict the final smoking status of the patients in a six-month period. Sensitivity, specificity, accuracy, and area under receiver operating characteristic (ROC) curve (AUC or ROC value) were used to determine the performance of the models. We adopted the ANN model which reached a slightly better performance, with a sensitivity of 0.704, a specificity of 0.567, an accuracy of 0.640, and an ROC value of 0.660 (95% confidence interval (CI): 0.617–0.702) for prediction in smoking cessation outcome. A predictive model for smoking cessation was constructed. The model could aid in providing the predicted success rate for all smokers. It also had the potential to achieve personalized and precision medicine for treatment of smoking cessation.

Purpose We aimed to analyze the presence and extent of coronary artery disease in patients with newly detected diabetes mellitus. Methods Clinical health examinations of asymptomatic community-dwelling adults between 2008 and 2018 at a medical center in Taiwan were reviewed. Coronary computed tomography angiography was performed in 444 participants, of which 338, 54, and 52 were categorized as ‘without diabetes mellitus’, ‘newly detected diabetes mellitus’, and ‘known diabetes mellitus’, respectively. Results Prevalence of significant coronary artery disease (≥ 50% stenosis) was higher in participants with newly detected diabetes mellitus than participants without diabetes mellitus (40.7% vs 20.1%, p < 0.0001). Among those with coronary artery stenosis, the number of coronary vessels with significant obstruction (0.72 vs 0.42, p = 0.0147) was also higher in participants with newly detected diabetes mellitus. Using multiple logistic regression analysis, new detection of diabetes mellitus was identified as an independent risk factor for significant coronary artery disease (odds ratio: 2.153, 95% confidence interval: 1.112–4.166). Conclusion Asymptomatic patients with newly detected diabetes mellitus had higher prevalence and greater extent of coronary artery disease than those without diabetes mellitus. More attention should thus be paid to the assessment of coronary artery disease in patients with newly detected diabetes mellitus.