Search

This paper presents a robust device for automated screening of pediatric heart diseases based on our unique processing method in murmur characterization; the Arash-Band method. The present study modifies the Arash-Band method and employs output of the modified method in conjunction with the two other original techniques to extract indicative feature vectors for the screening. The extracted feature vectors are classified by using the support vector machine method. Results show that the proposed modifications significantly enhances performance of the Arash-Band in terms of the both accuracy and sensitivity as the corresponding effect sizes are sufficiently large. The proposed algorithm has been incorporated into an Android-based tablet to constitute an intelligent phonocardiogram with the automatic screening capability. In order to obtain confidence interval of the accuracy and sensitivity, an inferable statistical test is applied on our database containing the phonocardiogram signals recorded from 263 of the referrals to a hospital. The expected value of the accuracy/sensitivity is estimated to be 87.45 % / 87.29 % with a 95 % confidence interval of (80.19 % - 92.47 %) / (76.01 % - 95.78 %) exhibiting superior performance than a pediatric cardiologist who relies on conventional or even computer-assisted auscultation. [ABSTRACT FROM AUTHOR]

Approximately 300 million people are afflicted with asthma around the world, with the estimated death rate of 250,000 cases, indicating the significance of this disease. If not treated, it can turn into a serious public health problem. The best method to treat asthma is to control it. Physicians recommend continuous monitoring on asthma symptoms and offering treatment preventive plans based on the patient's control level. Therefore, successful detection of the disease control level plays a critical role in presenting treatment plans. In view of this objective, we collected the data of 96 asthma patients within a 9-month period from a specialized hospital for pulmonary diseases in Tehran. A new ensemble learning algorithm with combining physicians' knowledge in the form of a rule-based classifier and supervised learning algorithms is proposed to detect asthma control level in a multivariate dataset with multiclass response variable. The model outcome resulting from the balancing operations and feature selection on data yielded the accuracy of 91.66%. Our proposed model combines medical knowledge with machine learning algorithms to classify asthma control level more accurately. This model can be applied in electronic self-care systems to support the real-time decision and personalized warnings on possible deterioration of asthma control level. Such tools can centralize asthma treatment from the current reactive care models into a preventive approach in which the physician's therapeutic actions would be based on control level. [ABSTRACT FROM AUTHOR]