A novel method for dipper/non-dipper pattern classification in hypertensive and non-diabetic patients

Autor:	Hasan Erdal, Ali Serdar Fak, Abdulkadir Kayikli, Ahmet Fevzi Baba, Zehra Aysun Altikardes, Hayriye Korkmaz
Rok vydání:	2019
Předmět:	Adult Male dipper 0206 medical engineering Biomedical Engineering Biophysics Health Informatics Bioengineering 02 engineering and technology Biomaterials 03 medical and health sciences 0302 clinical medicine Diabetes Mellitus Humans Medicine Aged biology Artificial neural network business.industry Dipper Blood Pressure Determination Pattern recognition Patient data non-dipper Middle Aged biology.organism_classification 020601 biomedical engineering Circadian Rhythm ambulatory blood pressure monitoring classification attribute reduction Null (SQL) Hypertension Female Learning to rank Artificial intelligence Sleep business 030217 neurology & neurosurgery Research Article Information Systems Non diabetic
Zdroj:	Technology and Health Care
ISSN:	1878-7401 0928-7329
Popis:	Background In the classical process, it was proven that ABPM data were the most significant attributes both by physician and ranking algorithms for dipper/non-dipper pattern classification as mentioned in our previous papers. To explore if any algorithm exists that would let the physician skip this diagnosis step is the main motivation of the study. Objective The main goal of the study is to build up a classification model that could reach a high-performance metrics by excluding ABPM data in hypertensive and non-diabetic patients. Methods The data used in this research have been drawn from 29 hypertensive patients without diabetes in endocrinology clinic of Marmara University in 2011. Five of 29 patient data were later removed from the dataset because of null data. Results The findings showed that dipper/non-dipper pattern can be classified by artificial neural network algorithms, the highest achieved performance metrics are accuracy 87.5%, sensitivity 71%, and specificity 94%. Conclusions This novel method uses just two attributes: Ewing-score and HRREP. It offers a fast and low-cost solution when compared with the current diagnosis procedure. This attribute reduction method could be beneficial for different diseases using a big dataset.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::79a8fa9bf178a6748255cdb19ca2dca6 https://doi.org/10.3233/thc-199006 Zobrazit plný text záznamu Plný text ve formátu PDF Plný text ve formátu HTML
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