Applications of Artificial Intelligence Methods for the Prediction of Osteoporotic Fractures

Abstract

Background: Osteoporosis is a socio-economic problem of modern aging societies. Bone fractures and the related treatments generate the highest costs. The occurrence of osteoporotic fractures is a cause of chronic disability, many complications, reduced quality of life, and often premature death. Aim of the study: The aim of the study was to determine which of the patient's potential risk factors pertaining to various diseases and lifestyle have an essential impact on the occurrence of low-energy fractures and the hierarchy of these factors. Methods: The study was retrospective. The documentation of 222 patients (206 women and 16 men) from an osteoporosis treatment clinic in Łódź, Poland was analyzed. Each patient was described by a vector consisting of 27 features, where each feature was a different risk factor. Using artificial neural networks, an attempt was made to create a model that, based on the available data, would be able to predict whether the patient would be exposed to low-energy fractures. We developed a neural network model that achieved the best result for the testing data. In addition, we used other methods to solve the classification problem, i.e., correctly dividing patients into two groups: those with fractures and those without fractures. These methods were logistic regression, k-nearest neighbors and SVM. Results: The obtained results gave us the opportunity to assess the effectiveness of various methods and the importance of the features describing patients. Using logistic regression and the recursive elimination of features, a ranking of risk factors was obtained in which the most important were age, chronic kidney disease, neck T-score, and serum phosphate level. Then, we repeated the learning procedure of the neural network considering only these four most important features. The average mean squared error on the test set was about 27% for the best variant of the model. Conclusions: The comparison of the rankings with different numbers of patients shows that the applied method is very sensitive to changes in the considered data (adding new patients significantly changes the result). Further cohort studies with more patients and more advanced methods of machine learning may be needed to identify other significant risk factors and to develop a reliable fracture risk system. The obtained results may contribute to the improved identification patients at risk of low-energy fractures and early implementation of comprehensive treatment.

Keywords: artificial neural networks; fractures; logistic regression; medical records; osteoporosis; risk factors.

Figure 1

The best neural network for 177 patients. The input is composed of 27 features. The first layer (hidden layer) has 20 neurons; for simplicity, only one of them is shown in the diagram. The second layer (output layer) has 1 neuron. The output has 1 parameter (prediction of fractures before treatment).

Figure 2

Flowchart for the network training procedure. A hidden layer is any layer other than the output layer. The maximum numbers of hidden layers and neurons was determined according to the size of the dataset.

Figure 3

PCA outcomes for the expanded set of 207 patients. Each dot represents a patient’s feature vector transformed into a two-dimensional space. Varying colors are used to differentiate between patient groups: those who experienced fractures and those who did not.

Figure 4

Outcomes of t-SNE application on the dataset of 207 patients. Every dot signifies a feature vector related to one patient expressed within a two-dimensional framework. Variations in colors are employed to distinguish different patient groups (those with fractures and those without).

Figure 5

The best neural network for 207 patients. The input is composed of 4 features (age, chronic kidney disease, neck T-score, and phosphates). The first layer (hidden layer) has 3 neurons. The second layer (output layer) has 1 neuron. The output has 1 parameter (prediction of fractures before or during treatment).