Background & Objectives: Cardiovascular disease is a leading cause of death worldwide. ECG signals are used to diagnose it. This study aims to eliminate signal noise by converting available wavelets and extracting existing waves. The location-related properties and amplitude of these waves will be extracted to develop a model based on the random forest algorithm for training and evaluating the algorithm. Materials & Methods: This study uses the MIT-BIH dataset, which contains digital ECG signals extracted from Holter bands for different patients at Arrhythmia Hospital from 1975 to 1979. The study applies signal processing and machine learning techniques to classify ECG signals and identify HEART patients. The MATLAB software implemented the algorithm, which was evaluated based on accuracy, error rate, TP, FP, Precision, Recall, F-Measure, and ROC criteria. These criteria were determined by a confusion matrix. Results: The study results and comparisons demonstrate that the proposed method is highly effective in detecting HEART patients. The proposed method's accuracy was found to be 99%, which is higher than other machine learning methods. Conclusion: The proposed method achieved an accuracy of 99. 1957%, surpassing other machine learning methods like support vector machine, neural network, and Bayes.

Background: One of the important DISEASES in the population is HEART disease. Congenital HEART disease complicated approximately one percent of all live births. Acute rheumatic fever involve the HEART which can be fatal during the acute stage or lead to rheumatic HEART disease, a chronic condition due to scarring and deformity of the HEART valves.Materials and Methods: In this research have been investigated about the value of chest-X-Ray in 74 patient in the DIAGNOSIS of HEART disease in patients of Modarres hospital during year of 1379 and decrypted radiographic appearances of HEART disease through enlargement of cardiac chambers, vascularity of lung and final DIAGNOSIS of HEART disease.Results: Results of this research may be play a role in the planning for DIAGNOSIS of HEART DISEASES and answer to this question that is it any role for CXR PA that is a simple and routine investigation for patients. The sensitivity and specificity of conventional radiology in the DIAGNOSIS of left to right shunt is 96.15 and 90.47 percent respectively and positively and negative predictive value is 86.2 and 97.43 percent respectively. In left atrial enlargement was 96.96 and 82.35 percent respectively and positive and negative predictive value is 84.2 and 96.55 percent respectively. In left ventricular enlargement was 73.3 and 82.5 percent respectively and positive and negative predictive value is 75.86 and85.5 percent respectively. In right atrial enlargement was 53.3 and 100 percent respectively and positively and positive and negative predictive value is-100 and 8735 percent respectively. In right ventricular enlargement is 86.95 and 72.72 percent respectively and positive and negative. The final DIAGNOSIS of HEART predictive value is 89.95 and 72.72 percent respectively.Conclusion: Disease with conventional radiology consists of: 28.37 percent true definite DIAGNOSIS, 58.1 percent true differential DIAGNOSIS and 13.5 percent false DIAGNOSIS.

The present study presents an intelligent procedure for simultaneous DIAGNOSIS Avian Newcastle Disease Virus, Infection Bronchitis Virus as well as Influenza through HEART sound signals. To follow the aim, the sample chickens were divided into four groups. The first group was taken as the control. The second, third and fourth groups were respectively infected with Newcastle Disease Virus, Infection Bronchitis as well as Avian Influenza. The time domain signals were transferred to the frequency and time-frequency domain using Fast Fourier and Discrete Wavelet Transforms. In data mining stage, 25 statistical features were extracted from three domains and the most appropriate features selected, using Improved Distance Evaluation (IDE) method. The HEART sound signals were classified using multiclass support vector machine and Dempster- Shafer Evidence Theory. Total accuracy, Specificity, and Sensitivity of classifiers, fusion in DIAGNOSIS of avian DISEASES were obtained as 81.93, 93.29 and 82.28 percent respectively.

Background: HEART DISEASES are complex pathophysiologic conditions involving biomarkers. Understanding the mechanisms by which a gene selectively triggers intracellular molecular responses provide insight into the complex processes implicated in HEART DISEASES. The aim of this study was to predict HEART DISEASES associated genes. Materials and Methods: A number of computational methods have been developed for human gene prioritization. In this study, we used Beegle and KEGG pathway databases and two online services for gene prioritization and analysis of genes related to HEART disease. Results: Over 200 genes and 5 key signaling pathways related to human HEART DISEASES were found. The processes in which gene mutations trigger a response in cells leading to cardiac conditions involve multiple pathways. Conclusion: The genes related to HEART DISEASES could be CRP, NPPB, IL-6, ACE2 and GATA4 with high scores and the researchers should find the diagnostic biomarker between them.

Background: Magnetic resonance imaging (MRI) is a relatively new medical technology with various applications. This study aims to evaluate the performance of this technology in DIAGNOSIS and treatment of HEART and neurological DISEASES.Methods: In this systematic review, electronic databases including the Cochrane library (DARE, NHS EEDs, CENTRAL and Cochrane systematic reviews), MEDLINE5 EMBASE and TRIP were searched which retrieved 25 articles. Inclusion criteria were studies in which MRI 3 Tesla was compared with a reference standard method including MRI 1.5 Tesla, and the outcomes were sensitivity, specificity, signal to noise ratio (SNR) and safety.Findings: 25 papers were included. Most of them had a relatively good quality. The majority showed that the diagnostic performance (sensitivity and specificity) of 3 Tesla was higher than 1.5 Tesla. The SN of 3 Tesla varied between 79 and 91% compared to 79 and 90% for 1.5 Tesla. The SP of 3 Tesla varied between 76 and 95% for 3 Tesla compared to 67 and 87% for 1.5 Tesla. Most of studies showed that the technical quality of images was higher with 3 Tesla compared to 1.5 Tesla. Both 1.5 and 3 Tesla were safe although 3 Tesla led to slightly more sensory stimuli.Conclusion: The diagnostic and technical performance of 3 Tesla is slightly higher than 1.5 Tesla.3 Tesla is slightly better in DIAGNOSIS of some specific cases.

Background and Aim Most HEART DISEASES show symptoms on ECG, but diagnosing HEART disease with ECG requires the knowledge and experience of medical specialized. Because these specialists may not always be available, it is necessary to design tools to diagnose HEART disease in these situations. In this paper, a two-stage approach based on artificial neural networks is designed to diagnose HEART disease using ECG information. In this study, we aim to propose a two-stage approach using artificial neural network (ANN) to diagnose HEART disease based ECG data. Methods & Materials To design the proposed approach, first the ECG data of 861 patients referred to medical centers in Arak, Iran were collected. The data were examined based on the opinions of specialists. Then, 154 features from ECG were used as inputs to the proposed model. In the first stage, an ANN was used to detect the ECG status (usable and unusable). In the second stage, using the usable ECG data, an ANN was used to diagnose the presence or absence of HEART disease. Finally, the performance of the two-stage approach was evaluated and its accuracy and precision in determining the ECG quality and HEART disease DIAGNOSIS were determined. Ethical Considerations This study was approved by the ethics committee of Arak University of Medical Sciences (Code: IR. ARAKMU. REC. 1400. 138). Results In the proposed approach, the ANN used for the determining the ECG status had a precision of 97. 1% and an accuracy of 97. 3%. The ANN used for the DIAGNOSIS of HEART disease had a precision of 95. 8% and an accuracy of 95. 4%. Conclusion Considering the high efficiency of the proposed approach in determining of ECG status and diagnosing HEART disease, it is possible to use this approach to help the treatment staff.

Introduction: The relationship between personality characteristics and coronary artery disease was more concerned since 1950. Studies some of personality factors can be considered as a predictor for higher risk of these DISEASES.Methods: In this descriptive study, eysenck personality questionnaire was given to all 126 myocardial infarction or angina pectoris patients who were admitted In Bandar Abbas Shahid Mohammadi hospital in CCU and cardiology ward during a 3 months period the questionnaire was randomly given to 52 patient keepers who had no history of cardiac disease, hyperlipidemia and hypertension as a control group. The results were analyzed with EPI5 software and using Chi-Square statistical test.Results: There were significant differences in E scale (extroversion-introversion) (P<0.05) and N scale (emotional stability - neurotic) (P<0.001-P<0.025) between the two groups.Conclusion: Personality trials should be considered in the treatment process of coronary artery disease.

Objectives: HEART DISEASES are among the most common DISEASES, subjecting the maternal and fetal life to risks, causing complications for both mothers and babies. Despite medical and surgical progress made in the HEART DISEASES, coronary artery DISEASES are still the second cause of the mortality and significant disability, as well as the low efficiency in the women over 40 years of age; also, they are the leading cause of mortality in the women over 65 years old. This study was conducted to investigate the prevalence of HEART DISEASES in the pregnant women. Materials and Methods: This study was a descriptive cross-sectional conducted on the pregnant women with HEART problems using the census method from September 2015 until September 2017. After data collection, they were analyzed using SPSS version 17. Results: The findings of the present study showed that dyspnea (28. 1%), cardiac palpation (32. 7%) and hypertension (11. 7%) were the most common causes of pregnant women’ s referral to obstetrics clinics. Also, most cardiac problems were associated with valvular problems (24. 3%) and HEART failure (18%). The average age of the patients was 28 ± 9 years. Conclusion: HEART DISEASES are very dangerous during pregnancy, but its progression and complications in the mother and the fetus can be prevented by treatment and continuous, as well as, a complete care during pregnancy and before delivery.