A Deep Neural Network-Based Method for Early Detection of Osteoarthritis Using Statistical Data

Abstract

A large number of people suffer from certain types of osteoarthritis, such as knee, hip, and spine osteoarthritis. A correct prediction of osteoarthritis is an essential step to effectively diagnose and prevent severe osteoarthritis. Osteoarthritis is commonly diagnosed by experts through manual inspection of patients' medical images, which are usually collected in hospitals. Checking the occurrence of osteoarthritis is somewhat time-consuming for patients. In addition, the current studies are focused on automatically detecting osteoarthritis through image-based deep learning algorithms. This needs patients' medical images, which requires patients to visit the hospital. However, medical utilization and health behavior information as statistical data are easier to collect and access than medical images. Using indirect statistical data without any medical images to predict the occurrence of diverse forms of OA can have significant impacts on pro-active and preventive medical care. In this study, we used a deep neural network for detecting the occurrence of osteoarthritis using patient's statistical data of medical utilization and health behavior information. The study was based on 5749 subjects. Principal component analysis with quantile transformer scaling was employed to generate features from the patients' simple background medical records and identify the occurrence of osteoarthritis. Our experiments showed that the proposed method using deep neural network with scaled PCA resulted in 76.8% of area under the curve (AUC) and minimized the effort to generate features. Hence, this methos can be a promising tool for patients and doctors to prescreen for possible osteoarthritis to reduce health costs and patients' time in hospitals.

Keywords: deep learning; feature extraction; osteoarthritis; prediction.

Figure 1

The selection process for the study population (KNHANES: Korea National Health and Nutrition Examination Survey).

Figure 2

The proposed system architecture of the deep neural network (DNN) with scaled principal component analysis (PCA).

Figure 3

Two-dimensional plots of first and second principal components. (a) Unscaled-PCA. (b) Min-max scaling with PCA. (c) Standard scaling with PCA. (d) Quantile transformer scaling with PCA.

Figure 4

Receiver operating characteristic (ROC) curve for the predictive performance of DNN with scaled-PCA and area under the curve (AUC) of 76.8%.