Automatic Feature Selection for Stenosis Detection in X-ray Coronary Angiograms

The automatic detection of coronary stenosis is a very important task in computer aided diagnosis systems in the cardiology area. The main contribution of this paper is the identification of a suitable subset of 20 features that allows for the classification of stenosis cases in X-ray coronary images with a high performance overcoming different state-of-the-art classification techniques including deep learning strategies. The automatic feature selection stage was driven by the Univariate Marginal Distribution Algorithm and carried out by statistical comparison between five metaheuristics in order to explore the search space, which is O(249) computational complexity. Moreover, the proposed method is compared with six state-of-the-art classification methods, probing its effectiveness in terms of the Accuracy and Jaccard Index evaluation metrics. All the experiments were performed using two X-ray image databases of coronary angiograms. The first database contains 500 instances and the second one 250 images. In the experimental results, the proposed method achieved an Accuracy rate of 0.89 and 0.88 and Jaccard Index of 0.80 and 0.79, respectively. Finally, the average computational time of the proposed method to classify stenosis cases was ≈0.02 s, which made it highly suitable to be used in clinical practice.

Predicting Health Information Suitability for Children Using Machine-Learning Assisted Selection of Semantic Features (Preprint)

BACKGROUND Suitability of health resources for specific readerships represents a critical yet underexplored area of research in health informatics, despite its importance in health literacy and health education. High relevance of health information can improve the suitability and readability of online health educational resources for young readers. It has an important role in developing the health literacy of children with increasing exposure to online health information. Existing research on health resource evaluation is limited to the analysis of the morphological and syntactic complexity. Besides, empirical instruments do not exist to evaluate the suitability of online health information for children. OBJECTIVE We aimed to develop algorithms to predict suitability of online health information for this understudied user group, using a small number of semantic features to provide accurate and convenient tools for automatic prediction of the suitability of online health information for children. METHODS Combining machine learning and linguistic insights, we identified semantic features to predict the suitability of online health information for children, as an emerging and large readership on online health information. The selection of natural language features as predicator variables of algorithms went through initial automatic feature selection using Ridge Classifier, support vector machine, extreme gradient boost, followed by revision by linguists, education experts based on effective health information design. We compared algorithms using the automatically selected features (19) and linguistically enhanced features (20), using the initial features (115) as the baseline. RESULTS Using 5-fold cross-validation, comparing with the baseline (115 features), the Gaussian Naive Bayes model (20 features) achieved statistically higher mean sensitivity (P =0.0206, 95% CI: -0.016, 0.1929); mean specificity (P = 0.0205, 95% CI: -0.016, 0.199); mean AUC (P =0.017, 95% CI: -0.007, 0.140); mean Macro F1 (P =0.0061, 95% CI: 0.016, 0.167). The statistically improved performance of the final model (20 features) stands in contrast with the statistically insignificant changes between the original feature set (115) and the automatically selected features (19): mean sensitivity (P =0.134, 95% CI: -0.1699, 0.0681), mean specificity (P = 0.1001, 95% CI: -0.1389, 0.4017); mean AUC (P =0.0082, 95% CI: 0.0059, 0.1126), and mean macro F1 (P = 0.9796, 95% CI: -0.0555, 0.0548). This demonstrates the importance and effectiveness of combing automatic feature selection and expert-based linguistic revision to develop most effective machine learning algorithms from high-dimensional datasets. CONCLUSIONS Our study developed machine learning algorithms for evaluating health information suitability for children, an important readership who is having increasing reliance on online health information for developing their health literacy. User-adaptive automatic assessment of online health contents holds much promise for distant and remote health education among young readers. Our study leveraged the precision, adaptability of machine learning algorithms and insights from health linguistics to help advance this significant yet understudied area of research.