Evaluation of health-related knowledge, attitudes, and behaviors of undergraduate students by cardiovascular risk factors.

AIM: To determine the presence of cardiovascular (CV) risk (CVR) factors in university students and evaluate how these factors are affected from the knowledge, attitudes, and habits of the individuals regarding healthy lifestyle. BACKGROUND: Starting from early ages, lifestyle habits such as lack of physical activity, unhealthy eating, and inappropriate drug use increase CV and metabolic risks of individuals. METHODS: In April-May 2018, sociodemographic characteristics of 770 undergraduate students, in addition to their knowledge, attitudes, and habits regarding their nutrition and physical activity status were obtained through face-to-face questionnaires. CVR factors were determined according to blood pressure, blood glucose, total cholesterol levels, and anthropometric measurements. Collected data were compared by CVR factor presence (CV[+] or CV[-]) in students. FINDINGS: The mean age of the participants was 22.3 ± 2.6 years. 59.6% were female and 71.5% were students of non-health sciences. In total, 274 individuals (35.9%) belonged to CV(+) group (mean risk number: 1.3 ± 0.5) with higher frequency in males (42.1% versus 31.6%, P < 0.05). The most common CVR factors were smoking (20.6%), high total cholesterol (7.5%), and hypertension/high blood pressure (6.0%). 15.5% of the participants regularly used at least one drug/non-pharmaceutical product. 11.3% complied the Mediterranean diet well. 21.9% of CV(+) stated consuming fast food at lunch compared to 14.3% of CV(-) (P < 0.05). 44.6% stated exercising below the CV-protective level. CONCLUSIONS: This study showed one-third of university students was at CVR, independent of their sociodemographic characteristics. Furthermore, the students appear to perform below expectations in terms of nutrition and physical activity. Extensive additional measures are needed to encourage young individuals for healthy nutritional and physical activity habits.

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

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.

Fuzzy logic based risk assessment system giving individualized advice for metabolic syndrome and fatal cardiovascular diseases.

In 2005, global cardiovascular diseases caused 30% of deaths in Europe, which is 46% of total deaths for all death groups. Today, according to the International Adult Diabetes Federation, 20% to 25% of the adult population in the world has Metabolic Syndrome. Turkish Statistical Institute claims that in Turkey 408782 people died of circulatory system diseases in 2016 and it is expected that numbers will dramatically increase. In 2003, total worldwide healthcare budget of Diabetes Mellitus was up to 64.9 billion International Dollars with the continuing rise in prevalence, it is expected that total costs will increase to 396 billion International Dollars by 2025. The main purpose of this study was to present a clinical decision support system that calculates Metabolic Syndrome existence and evaluate HeartScore risk level for Turkish population. The second objective was to create a detailed personal report about individual's risk level of Metabolic Syndrome and HeartScore and give advice to him/her to reduce it. The fuzzy logic risk assessment system (FLRAS) was formed in LabVIEW graphical development platform according to International Diabetes Federation and European Heart Journal's criteria. Mamdani type fuzzy logic sets were identified for each input variable and membership functions were assigned depending on the magnitude of the input limits. System's performance was tested on 96 (72 females, 24 males) patient data. Results show that the proposed system was able to evaluate the Metabolic Syndrome risk with 0.9285 specificity, 0.92708 accuracy and 0.925 sensitivity.