Comparison of different predicting models to assist the diagnosis of spinal lesions

ABSTRACT

In neurosurgical or orthopedic clinics, the differential diagnosis of lower back pain is often time-consuming and costly. This is especially true when there are several candidate diagnoses with similar symptoms that might confuse clinic physicians. Therefore, methods for the efficient differential diagnosis can help physicians to implement the most appropriate treatment and achieve the goal of pain reduction for their patients.

In this study, we applied data-mining techniques from artificial intelligence technologies, in order to implement a computer-aided auxiliary differential diagnosis for a herniated intervertebral disc, spondylolithesis, and spinal stenosis. We collected questionnaires from 361 patients and analyzed the resulting data by using a linear discriminant analysis, clustering, and artificial neural network techniques to construct a related classification model and to compare the accuracy and implementation efficiency of the different methods.

Our results indicate that a linear discriminant analysis has obvious advantages for classification and diagnosis, in terms of accuracy.

We concluded that the judgment results from artificial intelligence can be used as a reference for medical personnel in their clinical diagnoses. Our method is expected to facilitate the early detection of symptoms and early treatment, so as to reduce the social resource costs and the huge burden of medical expenses, and to increase the quality of medical care.

KEYWORDS:

• Data mining
• spinal disease
• auxiliary diagnosis

Discourse statement

All authors declare that they have no conflict of interest situations (financial or personal interests) that may affect or appear to affect the impartiality and the integrity of the peer review process of the

Additional information

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.