Prediction model for the risk of osteoporosis incorporating factors of disease history and living habits in physical examination of population in Chongqing, Southwest China: based on artificial neural network

Considering the availability of physical examination data and questionnaire data as well as the operability of the study, this study selected a convenience sampling to distribute personal health status and lifestyle questionnaire designed by the research team (see more in Supplementary File 1) to the adults who got dual-energy X-ray absorptiometry in the Medical Examination Center of the First Affiliated Hospital of Chongqing Medical University. Participants who had previously been diagnosed with osteoporosis were excluded. Questionnaires with missing content, especially those with missing medical card numbers and obvious logical errors will be excluded from the analysis. In the end, 1419 questionnaires (all over 20 years old) were effectively returned, and the effective response rate was 86.2%. The inclusion process of participants in this study is shown in Fig. 1.

Well-trained staff conducted a questionnaire survey on all subjects to obtain their information. The questionnaire consists of three parts, general information (including name, gender, medical card number), personal health (including hyperthyroidism, hypertension, CHD, DM, chronic gastrointestinal disease, chronic renal failure, gout, and malignant tumor), and living habits (take calcium tablet, smoking status, drinking status, cooking status, the nature of work, the main mode of transportation to work, housework, and physical exercise). Physical examination (weight, height, waist circumference (WC), blood pressure) was measured with standard medical equipment. The participants’ bone mineral density (BMD) was measured using DXA (MEDIX DR 2D-fan beam densitometer; Medilink, France). Daily phantom scans were performed every morning for quality control, and all BMD scans were conducted by well-trained examiners using standardized procedures, according to the recommended protocols by manufacturers. Body mass index (BMI) was calculated as weight in kilograms divided by the square of height in meters (kg/m²).

Normal BMD was defined if the T-score was more than − 1.0, osteopenia was defined if the T-score was less than or equal to − 1.0 and more than − 2.5, and osteoporosis was defined if the T-score was less than or equal to − 2.5, according to the WHO criteria (1994) [9]. As osteoporosis and osteopenia were the similar entity in nature, and the number of osteoporosis cases were small in this study, osteoporosis and osteopenia were combined into one outcome, which is called low BMD [10]. A history of hyperthyroidism, hypertension, CHD, DM, chronic gastrointestinal disease, chronic renal failure, gout, and malignant tumor was defined as received treatment or taking medication. Drinking was dichotomized (current drinker vs former or nondrinker). Smoking was also dichotomized (current smoker vs former or nonsmoker). The questionnaire defined cooking as cooking more than five times a week; Housework as never, occasionally, often; Calcium tablets as taking it for more than seven consecutive days. The nature of work is divided into three levels: sedentary, mild labor, manual labor. Sedentary behaviors have been classified into a range of human endeavors that result in an energy expenditure of no more than 1.5 times’ resting energy expenditure [11]. Manual labor was mainly based on physical activity, which required strong physical strength, greater muscle contraction, and physical movement of the limbs in space [12]. Mild labor falls somewhere between this sedentary and manual labor. The main modes of transportation to work are divided into four categories: working from home, walking, taking the bus and driving. Physical exercise was defined as whether they have participated in physical exercise lasting more than 20 min each time in recent years. There were two possible answers - yes and no.

Baseline characteristics were statistically described as mean ± standard deviation (SD). Continuous variables were analyzed by analysis of variance (ANOVA), and categorical variables were tested by chi-squared test. The univariate logistic regression analysis was used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) of baseline characteristics related to osteoporosis. We used SAS statistical software (version 9.4; SAS Institute Inc., Cary, North Carolina) to analyze the data.

All eligible participants (N = 1419) were randomly divided into two sets: the training set (N1 = 993) and the test set (N2 = 426) in the ratio of 7:3, the process was based on deep learning of ANN for proportional division [13, 14]. The input variables of osteoporosis prediction model based on ANN, DBN, SVM were selected by univariate logistic model (P < 0.20) [15], and the input variables of osteoporosis prediction model based on combination heuristic were selected by GA-DT. These models required that all variable values be standardized to 0 to 1. Binary variables were divided into 0 and 1; Non-binary variables were standardized to the range of 0 to 1 by the formula: \( {X}_m=\frac{X_m-{X}_{min}}{X_{max}-{X}_{min}} \) . After four prediction models were established, we compared the risk assessment models’ performance through accuracy, sensitivity, specificity, and receiver operating characteristic curve (ROC) to select the optimal prediction model. ROC curve is a visualized optimal model, which shows the performance of binary classifiers by considering the sensitivity, specificity, and accuracy of the model [16, 17]. Unless specified, we used the significance level of 0.05 for all analyses. Use software R version 4.0.2 (R Foundation for Statistical Computing, Vienna, Austria) and MATLAB (version R2013b; MathWorks.Inc., U.S.A) to build prediction models.

Table 1 summarized the baseline characteristics of the normal BMD and the low BMD in all study populations. In total study population, 460 out of 1419 subjects had low BMD, while the other 959 were normal. Compared with the normal BMD group, the low BMD group subjects were older and had lower BMI (P < 0.001). In the low BMD group, the incidence of fracture was higher (10.64% vs. 17.61%) and CHD (0.94% vs. 3.91%), the difference was statistically significant (P < 0.001), but other diseases such as hyperthyroidism, DM, chronic gastrointestinal disease, chronic renal failure, gout, and malignant tumor showed no significant difference between the two groups. The two groups had statistically significant differences in drug use (such as calcium tablets and estrogen drugs) and living habits (such as smoking, drinking, cooking, the nature of work, the main mode of transportation to work, doing the household work and physical exercise). There were no significant differences between two groups in terms of diastolic blood pressure (DBP).

As shown in Table 2, the related risk factors of osteoporosis from using univariate logistic regression model based on all samples (N = 1419). From the univariate logistic regression model, we found significantly protective factors for osteoporosis: height (OR = 0.934, 95%CI:0.921–0.948), weight (0.950, 95%CI:0.939–0.961), BMI (OR = 0.913, 95%CI:0.879–0.948), take calcium tablet (OR = 0.431, 95%CI:0.334–0.558). Continuous physical exercise for 20 min a day is also a protective factor for osteoporosis (OR = 0.747, 95%CI:0.585–0.954). There were also risk factors: in terms of gender, women were more likely to suffer from osteoporosis than men (OR = 1.963, 95%CI:1.566–2.461). As we age, osteoporosis was more likely to occur, age (OR = 1.109, 95%CI = 1.094–1.124). SBP (OR = 1.010, 95%CI:1.004–1.015), history of fracture (OR = 1.796, 95%CI:1.309–2.462), CHD (OR = 4.299, 95%CI:1.916–9.644), current drinking (OR = 1.835, 95%CI:1.436–2.345).

We established an ANN model based on factors related to osteoporosis derived from univariate logistic regression analysis. Gender, age, BMI, SBP, disease history (including fracture, hypertension, CHD, DM, chronic gastrointestinal disease, gout) and living habits (including taking calcium tablet, smoking, drinking, cooking, the nature of work, the main mode of transportation to work, doing the housework, physical exercise) as the input layer of the model. The output variable was a binary variable of whether a person had osteoporosis. The ANN structure consisted of three layers (Fig. 2), and the parameters were selected according to previous related studies [6, 18]. Training parameters such as learning rate and momentum were set at their default values. The training function was based on the back propagation (BP) with momentum algorithm. The neural network was trained for 46,673 steps. Training parameters such as learning rate and momentum of the DBN model have also default values. Through model debugging, the optimal DBN model was six neurons in the hidden layer. The prediction model based on GA-DT included 13 input variables.

Table 3 compared four prediction models through accuracy, sensitivity, specificity, AUC. The four indicators of the ANN model were better than the DBN. SVM and GA-DT had higher sensitivity than the ANN model, but the specificity was extremely low. Figure 3 shown the AUC obtained from the training set and test set of the ANN model. The AUC of the training set was 0.901, and the AUC of the test set was 0.762. The AUC of the training set and the test set of the prediction model based on DBN, SVM, and GA-DT were 0.622, 0.618; 0.698, 0.627; 0.744, 0.724, respectively. Therefore, the well-trained ANN model had the highest accuracy and AUC, and can accurately predict the risk of an individual suffering from osteoporosis.