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

Abstract

Background: Osteoporosis is a gradually recognized health problem with risks related to disease history and living habits. This study aims to establish the optimal prediction model by comparing the performance of four prediction models that incorporated disease history and living habits in predicting the risk of Osteoporosis in Chongqing adults.

Methods: We conduct a cross-sectional survey with convenience sampling in this study. We use a questionnaire From January 2019 to December 2019 to collect data on disease history and adults' living habits who got dual-energy X-ray absorptiometry. We established the prediction models of osteoporosis in three steps. Firstly, we performed feature selection to identify risk factors related to osteoporosis. Secondly, the qualified participants were randomly divided into a training set and a test set in the ratio of 7:3. Then the prediction models of osteoporosis were established based on Artificial Neural Network (ANN), Deep Belief Network (DBN), Support Vector Machine (SVM) and combinatorial heuristic method (Genetic Algorithm - Decision Tree (GA-DT)). Finally, we compared the prediction models' performance through accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC) to select the optimal prediction model.

Results: The univariate logistic model found that taking calcium tablet (odds ratio [OR] = 0.431), SBP (OR = 1.010), fracture (OR = 1.796), coronary heart disease (OR = 4.299), drinking alcohol (OR = 1.835), physical exercise (OR = 0.747) and other factors were related to the risk of osteoporosis. The AUCs of the training set and test set of the prediction models based on ANN, DBN, SVM and GA-DT were 0.901, 0.762; 0.622, 0.618; 0.698, 0.627; 0.744, 0.724, respectively. After evaluating four prediction models' performance, we selected a three-layer back propagation neural network (BPNN) with 18, 4, and 1 neuron in the input layer, hidden and output layers respectively, as the optimal prediction model. When the probability was greater than 0.330, osteoporosis would occur.

Conclusions: Compared with DBN, SVM and GA-DT, the established ANN model had the best prediction ability and can be used to predict the risk of osteoporosis in physical examination of the Chongqing population. The model needs to be further improved through large sample research.

Keywords: Artificial neural network; Disease history; Living habits; Osteoporosis; Physical examination; Prediction model.

Fig. 1

The inclusion process of participants in this study

Fig. 2

Graphic representation of the basic architecture of ANN used in training set. x₁ represents age, x₂ represents gender, x₃ represents BMI, x₄ represents SBP, x₅ represents history of fracture, x₆ represents history of hypertension, x₇ represents history of CHD, x₈ represents history of DM, x₉ represents history of chronic gastrointestinal disease, x₁₀ represents history of gout, x₁₁ represents take calcium tablet, x₁₂ represents cooking, x₁₃ represents drinking alcohol, x₁₄ represents smoking, x₁₅ represents the nature of work, x₁₆ represents the main mode of transportation to work, x₁₇ represents do the household work, x₁₈ represents physical exercise, and y represents osteoporosis

Fig. 3

The receiver operating characteristic (ROC) curves obtained from the ANN in training set and test set