Journal articles: 'Early detection of heart disease'

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Zabeeulla, M., C. Sharma, and A. Anand. "Early Detection of Heart Disease Using Machine Learning Approach." CARDIOMETRY, no. 26 (March 1, 2023): 342–47. http://dx.doi.org/10.18137/cardiometry.2023.26.342347.

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Heart attack is one of the leading causes of morbidity in the worldwide population. Cardiovascular disease is one of the major diseases involved in clinical data analysis or one of the most important part for forecasting. Early detection of cardiovascular diseases can help to reduce high-risk condition for heart patients to make individual decisions for their lifestyle adjustments, mitigating the challenges. Early detection of heart disease has been explored in this study using a machine-learning approach. Additionally, we used sampling strategies to deal with disparate datasets. The overall risk is estimated using a variety of machine-learning techniques. On Kaggle, the Heart Disease dataset is accessible and open for all. In present study testing set used this dataset. The ultimate objective is to determine whether the patient has a “10-year risk of developing coronary heart disease” (CHD). The dataset contained thirteen features that provided patient data, and the authors used machine learning algorithms to diagnose cardiac problems with 98.8% accuracy.

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Whalley, Gillian. "Appropriate and early detection of rheumatic heart disease." Australasian Journal of Ultrasound in Medicine 23, no. 1 (February 2020): 3–4. http://dx.doi.org/10.1002/ajum.12203.

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Anika and Navpreet Kaur. "A Review on Heart Disease Detection Techniques." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 7 (July 30, 2017): 395. http://dx.doi.org/10.23956/ijarcsse/v7i7/0200.

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The paper exhibits a formal audit on early detection of heart disease which are the major cause of death. Computational science has potential to detect disease in prior stages automatically. With this review paper we describe machine learning for disease detection. Machine learning is a method of data analysis that automates analytical model building.Various techniques develop to predict cardiac disease based on cases through MRI was developed. Automated classification using machine learning. Feature extraction method using Cell Profiler and GLCM. Cell Profiler a public domain software, freely available is flourished by the Broad Institute's Imaging Platform and Glcm is a statistical method of examining texture .Various techniques to detect cardio vascular diseases.

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Hanok Hruday Mohan, Yekula, Peddaguravagari Thejaswi, and Vanajakshamma. "School Health Screening For Early Detection Of Obesity, Congenital Heart Disease And Rheumatic Heart Disease." Indian Heart Journal 74 (November 2022): S10—S11. http://dx.doi.org/10.1016/j.ihj.2022.10.164.

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Kandukuri, Kumar, and A. Sandhya. "Heart Stroke Detection Using KNN Algorithm." ECS Transactions 107, no. 1 (April 24, 2022): 18385–93. http://dx.doi.org/10.1149/10701.18385ecst.

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Diagnosis of heart diseases have been improved in recent days with the help of machine learning (ML). The early prediction of heart disease is possible by analyzing the important parameters with the help of data mining techniques. In this study, K-Nearest Neighborhood (KNN) is used for the classification of heart stroke with parameter weighting methods to improve accuracy and 11 parameters were identified for training the KNN algorithm. The result shows that the accuracy using the KNN algorithm (11 parameters) is more efficient to predict the early heart stroke detection. This proposed algorithm accuracy is found to be better than the existing algorithms like Random Forest and Decision Tree and has an accuracy of average of 91.4%.

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Nagavelli, Umarani, Debabrata Samanta, and Partha Chakraborty. "Machine Learning Technology-Based Heart Disease Detection Models." Journal of Healthcare Engineering 2022 (February 27, 2022): 1–9. http://dx.doi.org/10.1155/2022/7351061.

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At present, a multifaceted clinical disease known as heart failure disease can affect a greater number of people in the world. In the early stages, to evaluate and diagnose the disease of heart failure, cardiac centers and hospitals are heavily based on ECG. The ECG can be considered as a regular tool. Heart disease early detection is a critical concern in healthcare services (HCS). This paper presents the different machine learning technologies based on heart disease detection brief analysis. Firstly, Naïve Bayes with a weighted approach is used for predicting heart disease. The second one, according to the features of frequency domain, time domain, and information theory, is automatic and analyze ischemic heart disease localization/detection. Two classifiers such as support vector machine (SVM) with XGBoost with the best performance are selected for the classification in this method. The third one is the heart failure automatic identification method by using an improved SVM based on the duality optimization scheme also analyzed. Finally, for a clinical decision support system (CDSS), an effective heart disease prediction model (HDPM) is used, which includes density-based spatial clustering of applications with noise (DBSCAN) for outlier detection and elimination, a hybrid synthetic minority over-sampling technique-edited nearest neighbor (SMOTE-ENN) for balancing the training data distribution, and XGBoost for heart disease prediction. Machine learning can be applied in the medical industry for disease diagnosis, detection, and prediction. The major purpose of this paper is to give clinicians a tool to help them diagnose heart problems early on. As a result, it will be easier to treat patients effectively and avoid serious repercussions. This study uses XGBoost to test alternative decision tree classification algorithms in the hopes of improving the accuracy of heart disease diagnosis. In terms of precision, accuracy, f1-measure, and recall as performance parameters above mentioned, four types of machine learning (ML) models are compared.

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Amit Jain, Suresh Babu Dongala, and Aruna Kama. "Heart disease prediction using machine learning techniques." Open Access Research Journal of Engineering and Technology 3, no. 1 (July 30, 2022): 001–6. http://dx.doi.org/10.53022/oarjet.2022.3.1.0028.

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Heart diseases are commonly caused and when neglected becomes life threatening. So, early detection of the disease is very important and for diagnosis to save lives. There can be many parameters that are to be considered to predict the heart disease. Some of them are like age, cholesterol, blood pressure levels. Etc., here we are going to implement Machine Learning model to predict heart disease.

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Singh, Swati, Ankur Kaushal, Shashi Khare, and Ashok Kumar. "mga Genosensor for Early Detection of Human Rheumatic Heart Disease." Applied Biochemistry and Biotechnology 173, no. 1 (March 18, 2014): 228–38. http://dx.doi.org/10.1007/s12010-014-0836-z.

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Hara, Akira, Masayuki Niwa, Tomohiro Kanayama, Kei Noguchi, Ayumi Niwa, Mikiko Matsuo, Takahiro Kuroda, Yuichiro Hatano, Hideshi Okada, and Hiroyuki Tomita. "Galectin-3: A Potential Prognostic and Diagnostic Marker for Heart Disease and Detection of Early Stage Pathology." Biomolecules 10, no. 9 (September 4, 2020): 1277. http://dx.doi.org/10.3390/biom10091277.

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The use of molecular biomarkers for the early detection of heart disease, before their onset of symptoms, is an attractive novel approach. Ideal molecular biomarkers, those that are both sensitive and specific to heart disease, are likely to provide a much earlier diagnosis, thereby providing better treatment outcomes. Galectin-3 is expressed by various immune cells, including mast cells, histiocytes and macrophages, and plays an important role in diverse physiological functions. Since galectin-3 is readily expressed on the cell surface, and is readily secreted by injured and inflammatory cells, it has been suggested that cardiac galectin-3 could be a marker for cardiac disorders such as cardiac inflammation and fibrosis, depending on the specific pathogenesis. Thus, galectin-3 may be a novel candidate biomarker for the diagnosis, analysis and prognosis of various cardiac diseases, including heart failure. The goals of heart disease treatment are to prevent acute onset and to predict their occurrence by using the ideal molecular biomarkers. In this review, we discuss and summarize recent developments of galectin-3 as a next-generation molecular biomarker of heart disease. Furthermore, we describe how galectin-3 may be useful as a diagnostic marker for detecting the early stages of various heart diseases, which may contribute to improved early therapeutic interventions.

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Catur Andryani, S.Si., MSc., Dr Nur Afny, Muhamad Femy Mulya, Surnanto Surnanto, and M. Rizam Kusfandi. "Rancang Bangun Purwarupa Aplikasi Deteksi Dini Penyakit Jantung Berbasis Case Base Reasoning dengan Keamanan Data." Jurnal SISKOM-KB (Sistem Komputer dan Kecerdasan Buatan) 5, no. 1 (September 30, 2021): 66–73. http://dx.doi.org/10.47970/siskom-kb.v5i1.230.

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The prevalence of heart disease has been consistently increasing in five recent years. In average 15 out of 1000 people have heart disease. Currently heart disease becomes the second leading cause of death in Indonesia. Early detection will guide the appropriate treatments to increase recovery opportunity. In another hand, many healthcare facilities in Indonesia are not equipped with the cardiologist. It triggers many heart disease cases are late to handle due late detection. Thus, we propose web based early heart disease detection application prototype using Case Base Reasoning framework. It is intended to support small clinic or other healthcare facilities which have no cardiologist to provide early detection of heart disease. The application is equipped with data security to handle the data privacy of the patient. Based on the black box evaluation by the expert, it is concluded that all the provided features can be run functionally.

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Muhammad, Yar, Moteeb Almoteri, Hana Mujlid, Abdulrhman Alharbi, Fahad Alqurashi, Ashit Kumar Dutta, Sultan Almotairi, and Hamad Almohamedh. "An ML-Enabled Internet of Things Framework for Early Detection of Heart Disease." BioMed Research International 2022 (September 21, 2022): 1–12. http://dx.doi.org/10.1155/2022/3372296.

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Healthcare occupies a central role in sustainable societies and has an undeniable impact on the well-being of individuals. However, over the years, various diseases have adversely affected the growth and sustainability of these societies. Among them, heart disease is escalating rapidly in both economically settled and undeveloped nations and leads to fatalities around the globe. To reduce the death ratio caused by this disease, there is a need for a framework to continuously monitor a patient’s heart status, essentially doing early detection and prediction of heart disease. This paper proposes a scalable Machine Learning (ML) and Internet of Things-(IoT-) based three-layer architecture to store and process a large amount of clinical data continuously, which is needed for the early detection and monitoring of heart disease. Layer 1 of the proposed framework is used to collect data from IoT wearable/implanted smart sensor nodes, which includes various physiological measures that have significant impact on the deterioration of heart status. Layer 2 stores and processes the patient data on a local web server using various ML classification algorithms. Finally, Layer 3 is used to store the critical data of patients on the cloud. The doctor and other caregivers can access the patient health conditions via an android application, provide services to the patient, and inhibit him/her from further damage. Various performance evaluation measures such as accuracy, sensitivity, specificity, F1-measure, MCC-score, and ROC curve are used to check the efficiency of our proposed IoT-based heart disease prediction framework. It is anticipated that this system will assist the healthcare sector and the doctors in diagnosing heart patients in the initial phases.

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Hartge, David R., Jan Weichert, Martin Krapp, Ute Germer, Ulrich Gembruch, and Roland Axt-Fliedner. "Results of early foetal echocardiography and cumulative detection rate of congenital heart disease." Cardiology in the Young 21, no. 5 (May 13, 2011): 505–17. http://dx.doi.org/10.1017/s1047951111000345.

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AbstractObjectiveThe aim of this study is to evaluate the cumulative detection rate of foetal echocardiography during gestation and in the early neonatal period, with a special emphasis on early foetal echocardiography.MethodsWe conducted a retrospective survey of all singleton pregnancies from 1993 to 2007, with complete sequential echocardiography from 11 plus 0 to 13 plus 6 weeks of gestation. It was mandatory to have at least one foetal echocardiography in the second or third trimester and one postnatally.ResultsOur study included 3521 pregnancies, in which 77 cases were diagnosed with congenital heart disease. Of them, 66 were detected in the first trimester – 11 plus 0 to 11 plus 6 weeks: 22 cases; 12 plus 0 to 12 plus 6 weeks: 23 cases; 13 plus 0 to 13 plus 6 weeks: 21 cases – with an 85.7% detection rate of congenital heart disease in early foetal echocardiography. In the second trimester, seven cases were found, with a detection rate of 9.1%. The third trimester reported two cases, with a detection rate of 2.6%. Postnatally, two (2.6%) cases were detected. The overall in utero detection rate of congenital heart disease was 97.4%.ConclusionsFoetal echocardiography performed at the time of anomaly screening in the first trimester results in high detection rates of congenital heart disease. Cardiac pathology may evolve, and further examinations at later stages of pregnancy could improve the detection rate of congenital heart disease.

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Santalova, Galina V., Andrei A. Garanin, Petr A. Lebedev, Maksim E. Kuzin, and Olga V. Tereshina. "Promising approaches to early diagnosis of chronic rheumatic heart disease." Science and Innovations in Medicine 7, no. 3 (September 4, 2022): 170–75. http://dx.doi.org/10.35693/2500-1388-2022-7-3-170-175.

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Aim to determine the prevalence of acute rheumatic fever (ARF) in children and chronic rheumatic heart disease (HRHD) in adults on the example of the Samara region and to propose measures for the early detection and prevention of valvular heart lesions. Material and methods. The authors cite the material of their own research, indicating a significant prevalence of rheumatic etiology of valvular lesions that required surgical treatment in 20162020 in the Samara region. We retrospectively analyzed the prevalence of acute rheumatic fever and chronic rheumatic heart disease in out-patients of a large polyclinic of Samara city. Results. The study demonstrates the low frequency of acute rheumatic fever in the Russian Federation along with the disproportionately high prevalence of chronic rheumatic heart disease. Conclusion. It is advisable to supplement the procedure for preventive medical examinations of the underage, has being in force in the territory of the Russian Federation since 2017, by ultrasound examination of the heart for the timely diagnosis of chronic rheumatic heart disease and long-term treatment with benzathine penicillin. In this case, it is also possible to identify previously undiagnosed congenital heart defects and heart diseases that manifest in adolescence.

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Arooj, Sadia, Saif ur Rehman, Azhar Imran, Abdullah Almuhaimeed, A. Khuzaim Alzahrani, and Abdulkareem Alzahrani. "A Deep Convolutional Neural Network for the Early Detection of Heart Disease." Biomedicines 10, no. 11 (November 3, 2022): 2796. http://dx.doi.org/10.3390/biomedicines10112796.

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Heart disease is one of the key contributors to human death. Each year, several people die due to this disease. According to the WHO, 17.9 million people die each year due to heart disease. With the various technologies and techniques developed for heart-disease detection, the use of image classification can further improve the results. Image classification is a significant matter of concern in modern times. It is one of the most basic jobs in pattern identification and computer vision, and refers to assigning one or more labels to images. Pattern identification from images has become easier by using machine learning, and deep learning has rendered it more precise than traditional image classification methods. This study aims to use a deep-learning approach using image classification for heart-disease detection. A deep convolutional neural network (DCNN) is currently the most popular classification technique for image recognition. The proposed model is evaluated on the public UCI heart-disease dataset comprising 1050 patients and 14 attributes. By gathering a set of directly obtainable features from the heart-disease dataset, we considered this feature vector to be input for a DCNN to discriminate whether an instance belongs to a healthy or cardiac disease class. To assess the performance of the proposed method, different performance metrics, namely, accuracy, precision, recall, and the F1 measure, were employed, and our model achieved validation accuracy of 91.7%. The experimental results indicate the effectiveness of the proposed approach in a real-world environment.

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Hamad, Aqeel, and Ammar Jasim. "Heart disease diagnosis based on deep learning network." Open Journal of Science and Technology 4, no. 1 (March 7, 2021): 1–9. http://dx.doi.org/10.31580/ojst.v4i1.1186.

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Heart disease is the leading cause of death, the cardiovascular disease (CVD) is the major cause of the death world wide according to world health organization. Over 30% of global death was because CVD. However it is considered as controllable disease, so early and accurate diagnosis of heart disease is essential to administrating early and optimal treatment in order to increase long –term survival. Early detection can lead to reduce disease progression. In this paper, we propose a new deep neural network that can be used as classifier in heart disease prediction system, the data base is splitted into training and testing parts, the training data are prepressed by extracting its features in order to perform data augmentation, then the augmented data are training by the designed new model that can increase the accuracy of heart disease detection. from the experimental results, the proposed model provide significant improvement in the prediction of the disease in terms of accuracy, sensitivity and specificity as compared with other approaches

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Willim, Herick Alvenus, Cristianto, and Alice Inda Supit. "Critical Congenital Heart Disease in Newborn: Early Detection, Diagnosis, and Management." Bioscientia Medicina : Journal of Biomedicine and Translational Research 5, no. 1 (December 15, 2020): 107–16. http://dx.doi.org/10.32539/bsm.v5i1.180.

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Critical congenital heart disease (CHD) is a type of CHD that requires early intervention in the first year of life to survive. Morbidity and mortality increases significantly if newborns with critical CHD experience delay in the initial diagnosis and management. The infants may develop cyanosis, systemic hypoperfusion, or respiratory distress as the main manifestations of critical CHD. Pulse oximetry screening for early detection of critical CHD must be performed in newborns after 24 hours of age or before discharge from hospital. Generally, infants with critical CHD require patency of the ductus arteriosus with infusion of prostaglandin to maintain pulmonary or systemic blood flow. After initial management, the infants must be immediately referred to tertiary care center for definitive intervention. Keywords: congenital heart disease, duct-dependent circulation, ductus arteriosus, prostaglandin

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Babu, Gokulnath Chandra, and Shantharajah S. P. "Remote Health Patient Monitoring System for Early Detection of Heart Disease." International Journal of Grid and High Performance Computing 13, no. 2 (April 2021): 118–30. http://dx.doi.org/10.4018/ijghpc.2021040107.

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This paper presents a heart disease prediction model. Among the recent technology, internet of things-enabled healthcare plays a vital role. The medical sensors used in healthcare provide a huge volume of medical data in a continuous manner. The speed of data generation in IoT healthcare is high so the volume of data is also high. In order to overcome this problem, the proposed model is a novel three-step process to store and analyze the large volumes of data. The first step focuses on a collection of data from sensor devices. In Step 2, HBase has been used to store the large volume of medical sensor data from a wearable device to the cloud. Step 3 uses Mahout for devolving logistic regression-based prediction model. At last, ROC curve is used to find the parameters that cause heart disease.

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Krithiga, B., P. Sabari, I. Jayasri, and I. Anjali. "Early detection of Coronary Heart Disease by using Naive Bayes Algorithm." Journal of Physics: Conference Series 1717 (January 2021): 012040. http://dx.doi.org/10.1088/1742-6596/1717/1/012040.

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WATTS, Gerald F., and Thomas H. MARWICK. "Ventricular dysfunction in early diabetic heart disease: detection, mechanisms and significance." Clinical Science 105, no. 5 (November 1, 2003): 537–40. http://dx.doi.org/10.1042/cs20030211.

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The detection of preclinical heart disease is a new direction in diabetes care. This comment describes the study by Vinereanu and co-workers in this issue of Clinical Science in which tissue Doppler echocardiography has been employed to demonstrate subtle systolic and diastolic dysfunction in Type II diabetic patients who had normal global systolic function and were free of coronary artery disease. The aetiology of early ventricular dysfunction in diabetes relates to complex intramyocardial and extramyocardial mechanisms. The initiating event may be due to insulin resistance, and involves abnormal myocardial substrate utilization and uncoupling of mitochondrial oxidative phosphorylation. Dysglycaemia plays an important role via the effects of oxidative stress, protein kinase C activation and advanced glycosylation end-products on inflammatory signalling, collagen metabolism and fibrosis. Extramyocardial mechanisms involve peripheral endothelial dysfunction, arterial stiffening and autonomic neuropathy. The clinical significance of the ventricular abnormalities described is unknown. Confirmation of their prognostic importance for cardiac disease in diabetes would justify routine screening for presymptomatic ventricular dysfunction, as well as clinical trials of novel agents for correcting causal mechanisms. These considerations could also have implications for patients with obesity and the metabolic syndrome.

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Ashraf, Haroon. "Early detection of heart disease in patients with type-I diabetes." Lancet 356, no. 9231 (August 2000): 741. http://dx.doi.org/10.1016/s0140-6736(05)73643-6.

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Watanabe, Shigeyuki, and Satsuki Yamada. "Magnetocardiography in Early Detection of Electromagnetic Abnormality in Ischemic Heart Disease." Journal of Arrhythmia 24, no. 1 (2008): 4–17. http://dx.doi.org/10.1016/s1880-4276(08)80002-6.

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Yogibuana Swastika Putri, Valerinna. "Early Detection of Congenital Heart Disease in Pregnant Young Women at Risk." Heart Science Journal 03, no. 03 (July 1, 2022): 1–3. http://dx.doi.org/10.21776/ub.hsj.2022.003.03.1.

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Cardiovascular disease (CVD) is one of the non-communicable diseases in which the leading cause of death worldwide at 48%1. CVD contains a spectrum of diseases, one of which is Congenital Heart Defect (CHD). CHD is one of the contributors to morbidity from young until adulthood. The advancement of surgical correction and medical therapy made it possible for early structural correction, as uncorrected CHD while patients were still young posed a risk of health outcome deterioration later in life. CHD affected especially pregnant individuals. A pregnant woman diagnosed with CHD would often complain of significantly significant deterioration of health condition and quality of life as they have a higher susceptibility to cardiovascular complications than women in general. Pregnant women diagnosed with CHD in developing countries more often presented with signs indicating complications of CHD, such as Eisenmenger syndrome, signs of heart failure, Pulmonary Hypertension (PH), cyanosis, and NYHA functional classes II and III, which indicate later diagnosis and treatment further into adulthood in these patients. Lack of expertise and facility for diagnosis of CHD posed a major challenge in reducing mortality related to CHD in these countries. Therefore the need for a screening method at least for directing further referral to major health centers is still in high demand.

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Kumar, Naresh, Nripendra Narayan Das, Deepali Gupta, Kamali Gupta, and Jatin Bindra. "Efficient Automated Disease Diagnosis Using Machine Learning Models." Journal of Healthcare Engineering 2021 (May 4, 2021): 1–13. http://dx.doi.org/10.1155/2021/9983652.

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Recently, many researchers have designed various automated diagnosis models using various supervised learning models. An early diagnosis of disease may control the death rate due to these diseases. In this paper, an efficient automated disease diagnosis model is designed using the machine learning models. In this paper, we have selected three critical diseases such as coronavirus, heart disease, and diabetes. In the proposed model, the data are entered into an android app, the analysis is then performed in a real-time database using a pretrained machine learning model which was trained on the same dataset and deployed in firebase, and finally, the disease detection result is shown in the android app. Logistic regression is used to carry out computation for prediction. Early detection can help in identifying the risk of coronavirus, heart disease, and diabetes. Comparative analysis indicates that the proposed model can help doctors to give timely medications for treatment.

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Nguyen, Timothy, and Zhe (Amy) Wang. "Cardiovascular Screening and Early Detection of Heart Disease in Adults With Chronic Kidney Disease." Journal for Nurse Practitioners 15, no. 1 (January 2019): 34–40. http://dx.doi.org/10.1016/j.nurpra.2018.08.004.

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Titma, T., U. Günther, C. Ludwig, M. Pikta, G. Zemtsovskaja, M. Viigimaa, and A. Samoson. "The metabolic pattern could be used for early detection of stable ischemic heart disease and hypertensive heart disease." Revue d'Épidémiologie et de Santé Publique 66 (July 2018): S328. http://dx.doi.org/10.1016/j.respe.2018.05.243.

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Hawal, Mr Sumit, and Dr Sandeep Dwarkanath Pande. "Predicting Heart Disease at Early Stages using Machine Learning: A Survey." Journal of University of Shanghai for Science and Technology 23, no. 09 (September 24, 2021): 1178–81. http://dx.doi.org/10.51201/jusst/21/09662.

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Cardiovascular disease diagnosis is the most difficult task in medicine. The diagnosis of heart disease is complicated because it requires the grouping of massive volumes of clinical and pathological data. As a result of this dilemma, researchers and clinical professionals have developed a strong interest in the efficient and exact prediction of heart disease. When it comes to heart disease, it is critical to obtain an accurate diagnosis at an early stage because time is of the essence. Heart disease is the largest cause of death worldwide, and early detection of heart disease is critical. Machine learning has evolved as one of the most progressive, dependable, and supportive tools in the medical field in recent years, providing the greatest assistance for disease prediction when properly trained and tested. The primary objective of this research is to evaluate several algorithms for heart disease prediction.

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Marijon, Eloi, and Xavier Jouven. "Early Detection of Rheumatic Heart Disease and Prevention of Heart Failure in Sub-Saharan Africa." Journal of the American College of Cardiology 51, no. 11 (March 2008): 1125–26. http://dx.doi.org/10.1016/j.jacc.2007.11.056.

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K.S, Anjana. "Heart Disease Prediction System." International Journal for Research in Applied Science and Engineering Technology 9, no. 9 (September 30, 2021): 1861–65. http://dx.doi.org/10.22214/ijraset.2021.38230.

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Abstract: Heart diseases are the one of the primary reasons of human death today. There are many recent technologies are used to assist the medical professionals and doctors in the prediction of heart disease in the early stage. Prediction of heart disease is a critical challenge in the area of clinical data analysis. This paper introduces a technique to detect arrhythmia, which is a representative type of cardio vascular diseases. Arrhythmia refers to any irregular change from the normal heart rhythms, means that your heart beats too quickly, too slowly, or with an irregular pattern. The Electro Cardiogram (ECG) is used as an input for the arrhythmia detection. It displays the rhythm and status of the heart. This paper propose an effective ECG arrhythmia classification approach based on a deep convolutional neural network (CNN), which has lately demonstrated remarkable performance in the field of machine learning. It perform the classification without any manual pre-processing of the ECG signals such as noise filtering, feature extraction, and feature reduction. Keywords: Arrhythmia, ECG, deep learning, CNN, ResNet

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C, Arjun, Anusha A, Meghana K, Nireeksha Amin, and Dr Pradeep B S. "PREDICTING CANCER AND HEART DISEASE AT EARLY STAGES USING MACHINE LEARNING." International Journal of Innovative Research in Advanced Engineering 9, no. 5 (May 31, 2022): 105–9. http://dx.doi.org/10.26562/ijirae.2022.v0905.001.

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People today suffer from a variety of diseases as a result of their living habits and their surroundings. As a result, predicting sickness at an early stage becomes a critical responsibility. Machine learning has been demonstrated to be useful in assisting with decision-making and prediction from enormous amounts of data generated by the health sector. As a result, machine learning's use in medical diagnostics is rapidly rising. Heart disease is one of the leading causes of death in the modern world. Predicting and detecting cardiac disease has always been a difficult issue for medical professionals. To treat cardiac disorders, hospitals and other clinics are offering costly medicines and operations. As a result, anticipating cardiac disease in its early stages will be beneficial to people all over the world, allowing them to take the essential steps before it becomes serious. Cancer has been described as a diverse illness with numerous subgroups. Early detection and prognosis of a cancer type has become a necessity in cancer research since it can help with patient clinical treatment. The health industry nowadays plays a key part in curing patient’s problems, thus this is also a way for the health sector to enlighten users. It is useful for the user if he or she does not want to go to the hospital or any other clinic, because by simply entering the symptoms and other relevant information, the user can learn about the disease they are suffering from. The health industry can also benefit from this system by simply asking the user for symptoms and entering them into the system, and in a matter of seconds, they can tell the exact and, to a degree, accurate diseases.

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Singh, Swati, Ankur Kaushal, and Ashok Kumar. "Recent advances in sensors for early detection of pathogens causing rheumatic heart disease." Sensor Review 38, no. 1 (January 15, 2018): 92–98. http://dx.doi.org/10.1108/sr-06-2017-0094.

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Purpose There is an immense concern in the international community about controlling the outburst of infectious diseases. An essential step towards diminishing it is the development of an adequate detection system. Among the huge plethora of microorganisms which may infect the human body, Streptococcus pyogenes is important one which infects the upper respiratory tract leading to sore throat, which eventually develops into rheumatic heart disease (RHD) in the absence of timely treatment. A major process in controlling the infection is to detect it at an early stage. Hence, there is a need to develop detection tools which are both rapid and reliable. Design/methodology/approach Different types of diagnostic methods are available for identification, but the most commonly used are culturing, staining and rapid antigen detection tests. For better sensitivity and specificity, this review describes the development of biosensor. Compared with the current available methods, which are usually cumbersome, time-consuming and expensive, this approach features sequence specificity, cost efficiency, rapid and ease of use. Findings This review outlines various sensors which are available for the detection of Streptococcus pyogenes which causes human RHD. The working scheme of the sensors, their sensitivity and limitation of detection has been described in the review. Originality/value The review fulfills an acknowledged the need to study various sensors that are available for the detection of Streptococcus pyogenes, causing human RHD.

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Petropoulos, Andreas. "Prevention and Early Detection of Congenital Heart Defects. Where do we stand." EURASIAN JOURNAL OF CLINICAL SCIENCES 1, no. 4 (April 8, 2021): 1–6. http://dx.doi.org/10.28942/ejcs.v1i4.41.

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Introduction: Since the origin of Medicine in 4th BC. Century research has taught us that learning and practicing preventive medicine is properly the best method to prevent disease from happening in the first place. Preventive health care must be planned and executed ahead of time, even when illness/ disease, is absent, especially for those that are common and fаtal. Among neonates and infants, congenital heart disease (CHD) is responsible for the largest proportion of mortality caused by birth defects. Actual numbers of patients and mortality resulting from CHD reportedly is increasing. In the developed world the treatment of CHD has escalating costs for health care systems and private covered patients, while in low-income countries it is not always available. Prevention is urgently needed to tackle the increasing needs. Aim: To present the current practice in preventing/early detecting CHD and justify why pulse oximetry is the best available, early detecting postnatal screening test we currently have. Methods: The existing in use preventing/early detecting methods for avoiding or early diagnosing CHD are: 1. Eliminate the maternal risk factors by obtaining a good level of health and medical surveillance during pregnancy. 2. Avoiding teratogenic agents, 3. Detecting risk factors from Family History, 4. Delivering a balanced Nutrition during Pregnancy 5. Obtaining at least an experienced 4-chamber view and outflow tracts imaging during the 20-weeks anomaly scan. 6. Fetal Echocardiography when indicated 7. Postnatal evaluation by experienced Pediatricians. 8. Pulse Oximetry, screening test after 72 hours post-delivery in term babies. 9. Hyperoxia test when indicated. Conclusion: Although CHD’s are the most common, high morbidity and mortality, congenital malformations, we still lack a single, easy to apply, non-invasive and low-cost screening test, for early detection. The current preventive methods must be combined to counterbalance the CHD prevalence. Meanwhile, they are costly and partially accessible. The most advantageous method for minimizing CHD deaths worldwide seems to be currently, pulse oximetry combined with clinical assessment. Original publication: Petropoulos AC. Prevention and Early Detection of Congenital Heart Defects. Where do we Stand. J Cardiol 2018, 2(1): 000111.

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Petropoulos, Andreas. "Prevention and Early Detection of Congenital Heart Defects. Where do we stand." EURASIAN JOURNAL OF CLINICAL SCIENCES 1, no. 5 (September 12, 2018): 1–6. http://dx.doi.org/10.28942/ejcs.v1i5.41.

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Introduction: Since the origin of Medicine in 4th BC. Century research has taught us that learning and practicing preventive medicine is properly the best method to prevent disease from happening in the first place. Preventive health care must be planned and executed ahead of time, even when illness/ disease, is absent, especially for those that are common and fаtal. Among neonates and infants, congenital heart disease (CHD) is responsible for the largest proportion of mortality caused by birth defects. Actual numbers of patients and mortality resulting from CHD reportedly is increasing. In the developed world the treatment of CHD has escalating costs for health care systems and private covered patients, while in low-income countries it is not always available. Prevention is urgently needed to tackle the increasing needs. Aim: To present the current practice in preventing/early detecting CHD and justify why pulse oximetry is the best available, early detecting postnatal screening test we currently have. Methods: The existing in use preventing/early detecting methods for avoiding or early diagnosing CHD are: 1. Eliminate the maternal risk factors by obtaining a good level of health and medical surveillance during pregnancy. 2. Avoiding teratogenic agents, 3. Detecting risk factors from Family History, 4. Delivering a balanced Nutrition during Pregnancy 5. Obtaining at least an experienced 4-chamber view and outflow tracts imaging during the 20-weeks anomaly scan. 6. Fetal Echocardiography when indicated 7. Postnatal evaluation by experienced Pediatricians. 8. Pulse Oximetry, screening test after 72 hours post-delivery in term babies. 9. Hyperoxia test when indicated. Conclusion: Although CHD’s are the most common, high morbidity and mortality, congenital malformations, we still lack a single, easy to apply, non-invasive and low-cost screening test, for early detection. The current preventive methods must be combined to counterbalance the CHD prevalence. Meanwhile, they are costly and partially accessible. The most advantageous method for minimizing CHD deaths worldwide seems to be currently, pulse oximetry combined with clinical assessment. Original publication: Petropoulos AC. Prevention and Early Detection of Congenital Heart Defects. Where do we Stand. J Cardiol 2018, 2(1): 000111.

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Jorquera-Chavez, Maria, Sigfredo Fuentes, Frank R. Dunshea, Robyn D. Warner, Tomas Poblete, Rebecca S. Morrison, and Ellen C. Jongman. "Remotely Sensed Imagery for Early Detection of Respiratory Disease in Pigs: A Pilot Study." Animals 10, no. 3 (March 9, 2020): 451. http://dx.doi.org/10.3390/ani10030451.

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Respiratory diseases are a major problem in the pig industry worldwide. Due to the impact of these diseases, the early identification of infected herds is essential. Computer vision technology, using RGB (red, green and blue) and thermal infrared imagery, can assist the early detection of changes in animal physiology related to these and other diseases. This pilot study aimed to identify whether these techniques are a useful tool to detect early changes of eye and ear-base temperature, heart rate and respiration rate in pigs that were challenged with Actinobacillus pleuropneumoniae. Clinical observations and imagery were analysed, comparing data obtained from animals that showed some signs of illness with data from animals that showed no signs of ill health. Highly significant differences (p < 0.05) were observed between sick and healthy pigs in heart rate, eye and ear temperature, with higher heart rate and higher temperatures in sick pigs. The largest change in temperature and heart rate remotely measured was observed around 4–6 h before signs of clinical illness were observed by the skilled technicians. These data suggest that computer vision techniques could be a useful tool to detect indicators of disease before the symptoms can be observed by stock people, assisting the early detection and control of respiratory diseases in pigs, promoting further research to study the capability and possible uses of this technology for on farm monitoring and management.

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Selvan, Saravana, S. John Justin Thangaraj, J. Samson Isaac, T. Benil, K. Muthulakshmi, Hesham S. Almoallim, Sulaiman Ali Alharbi, R. R. Kumar, and Sojan Palukaran Thimothy. "An Image Processing Approach for Detection of Prenatal Heart Disease." BioMed Research International 2022 (August 2, 2022): 1–14. http://dx.doi.org/10.1155/2022/2003184.

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Prenatal heart disease, generally known as cardiac problems (CHDs), is a group of ailments that damage the heartbeat and has recently now become top deaths worldwide. It connects a plethora of cardiovascular diseases risks to the urgent in need of accurate, trustworthy, and effective approaches for early recognition. Data preprocessing is a common method for evaluating big quantities of information in the medical business. To help clinicians forecast heart problems, investigators utilize a range of data mining algorithms to examine enormous volumes of intricate medical information. The system is predicated on classification models such as NB, KNN, DT, and RF algorithms, so it includes a variety of cardiac disease-related variables. It takes do with an entire dataset from the medical research database of patients with heart disease. The set has 300 instances and 75 attributes. Considering their relevance in establishing the usefulness of alternate approaches, only 15 of the 75 criteria are examined. The purpose of this research is to predict whether or not a person will develop cardiovascular disease. According to the statistics, naïve Bayes classifier has the highest overall accuracy.

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Petrovic, Dejan, Vladimir Miloradovic, Mileta Poskurica, and Biljana Stojimirovic. "Heart failure in haemodialysis patients: Evaluation and treatment." Srpski arhiv za celokupno lekarstvo 139, no. 3-4 (2011): 248–55. http://dx.doi.org/10.2298/sarh1104248p.

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Cardiovascular diseases are the leading cause of death in patients on haemodialysis. Cardiovascular mortality rate in these patients is approximately 9% per year, with the highest prevalence of left ventricular hypertrophy, ischemic heart disease and congestive heart failure being the most frequent cardiovascular complications. Risk factors for cardiac failure include hypertension, disturbed lipid metabolism, oxidative stress, microinflammation, hypoalbuminemia, anaemia, hyperhomocysteinemia, and increased concentration of asymmetric dimethylarginine, increased shunt blood flow and secondary hyperparathyroidism. Diagnostic strategy for early detection of patients with increased risk for the development of asymptomatic disturbances of systolic and diastolic left ventricular function should include echocardiografic examination, tests for determining coronary vascular disease, as well as tests of myocardial function (BNP, Nt-proBNP). Early detection of patients with a high risk of congestive heart failure enables timely implementation of adequate therapeutic strategy to provide high survival rate of HD patients.

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P, Ajaay Krishna, Akhilesh R, Aravind C J, and Dr K. Rama Abirami. "Early Prediction of Cardiac Disease using Expert Systems." International Journal of Recent Technology and Engineering (IJRTE) 11, no. 1 (May 30, 2022): 140–45. http://dx.doi.org/10.35940/ijrte.a6981.0511122.

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Machine learning is effective in helping and making selections from the high volumes of data created by the healthcare business. In this work, completely different classification algorithms are applied with their own advantage on separate databases of malady accessible for disease prediction. The results of the study strengthen by using Artificial Intelligence in the early detection of diseases and this will increase the survival rate of patients considerably. The motivation of this paper is to develop efficacious treatment of data processing techniques that will facilitate remedial things. Data processing classification algorithms are used to diagnose heart diseases.

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Hussain, Iftikhar, Huma Qayyum, Raja Rizwan Javed, Farman Hassan, and Auliya Ur Rahman. "Detection of Coronary Artery Using Novel Optimized Grid Search-based MLP." Vol 4 Issue 1 4, no. 1 (March 16, 2022): 276–87. http://dx.doi.org/10.33411/ijist/2022040121.

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In recent years, we have witnessed a rapid rise in the mortality rate of people of every age due to cardiac diseases. The diagnosis of heart disease has become a challenging task in present medical research, and it depends upon the history of patients. Rapid advancements in the field of deep learning. Therefore, it is a need to develop an automated system that assists medical experts in their decision-making process. In this work, we proposed a novel optimized grid search-based multi-layer perceptron method to effectively detect heart disease patients earlier and accurately. We evaluated the performance of our method on a dataset named Public Health dataset for heart diseases. More specifically, our method obtained an accuracy of 95.12%, precision of 95.32%, recall of 95.32%, and F1-score of 95.32%. We made a comparison of our method with existing methods to check superiority and robustness of our system to detect heart disease patients. Experimental results along with comprehensive comparison with other methods illustrate that our technique has superior performance and is robust to detect heart disease patients. From the results, we can conclude that our method is reliable to be used in hospitals for the early detection of heart disease patients.

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Dwi Andini, Syfa. "Review Analisis Hubungan Penyakit Jantung Koroner Terhadap Risiko Stres." Cerdika: Jurnal Ilmiah Indonesia 2, no. 11 (November 17, 2022): 933–37. http://dx.doi.org/10.36418/cerdika.v2i11.471.

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Coronary heart disease is a global health problem worldwide as it is the leading cause of death in cardiovascular disease. Cardiovascular disease is a condition where the heart and blood vessels do not function normally due to disorders, resulting in congenital heart disease, rheumatic heart disease, stroke and high blood pressure. According to the World Health Organization, the global prevalence of coronary heart disease increased to 23.3 million in 2010. Meanwhile, based on data from the American Heart Association (AHA) in 2010, it was the cause of 1 in every 6 deaths from coronary heart disease in the United States. Knowing the risk factors for a disease is very important in understanding early detection. Stress is one of the risk factors for coronary heart disease and is still widely considered trivial and gets less attention as early detection of the disease. This research is a literature study that will describe the relationship between stress risk factors and coronary heart disease. The purpose of this journal review is to determine the risk of stress disease. Literature findings from previous studies suggest that stress is associated with or affects the risk of coronary heart disease.

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ASHWORTH, MICHAEL. "FETAL CARDIAC DISEASE: THE PATHOLOGIST'S PERSPECTIVE." Fetal and Maternal Medicine Review 24, no. 2 (May 2013): 60–75. http://dx.doi.org/10.1017/s0965539513000053.

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In the European Union, it is estimated that 36,000 children are born every year with congenital heart disease and that a further 3000 who are diagnosed with congenital heart disease die as a result of termination of pregnancy, late fetal death or early neonatal death. In a normal population, the risk of a woman having a child with a congenital heart malformation is of the order of 0.8–1%, the risk rising to 2–3%, if a previous pregnancy was affected by heart disease and approaching 6% if the mother herself has a congenital heart defect. There is great variation between countries in the antenatal detection of heart defects, being lowest in those countries without ultrasound antenatal screening programmes (8–11%), but in Western Europe the detection rates vary between 19% and 48%. High-resolution echocardiography enables assessment of precise structures during the second trimester or even earlier.

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Trucco, Sara M., Joaquin Barnoya, Luis Alesandro Larrazabal, Aldo Castañeda, and David F. Teitel. "Detection rates of congenital heart disease in Guatemala." Cardiology in the Young 21, no. 2 (December 8, 2010): 153–60. http://dx.doi.org/10.1017/s1047951110001617.

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AbstractObjectivesIn developing countries, congenital heart disease is often unrecognised, leading to serious morbidity and mortality. Guatemala is one of the few developing countries where expert paediatric cardiac treatment is available and affordable, and therefore early detection could significantly improve outcome. We assessed regional congenital heart disease detection rates in Guatemala, and determined whether they correlated with the regional human development index.MethodsWe retrospectively reviewed all new cardiac referrals made in 2006 to the Unidad de Cirugia Cardiovascular Pediatrica, the only paediatric cardiac centre in Guatemala. We calculated regional detection rates by comparing the number of congenital heart disease referrals with the expected incidence using the National Ministry of Health birth data. We then compared the regional detection rates with the human development index data published in the United Nations 2006 Development Program Report using Spearman’s rank correlation.ResultsAn estimated 3935 infants with cardiac defects were born in Guatemala in 2006, an expected 1380 (35%) of whom had severe forms. Overall, only 533 children (14%) with cardiac defects were referred. Of these, 62% had simple shunt lesions, 13% had cyanotic lesions, and 10% had left-sided obstructive lesions. Only 11.5% of referred patients were neonates. Regional detection rates, ranged 3.2–34%, correlated with the regional human development index (r = 0.75, p < 0.0001).ConclusionsCurrent detection of congenital heart disease in Guatemala is low and correlates with the regional human development index. Those detected are older and have less severe forms, suggesting a high mortality rate among Guatemalan neonates with complex cardiac defects.

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Mokeddem, Sidahmed, and Baghdad Atmani. "Assessment of Clinical Decision Support Systems for Predicting Coronary Heart Disease." International Journal of Operations Research and Information Systems 7, no. 3 (July 2016): 57–73. http://dx.doi.org/10.4018/ijoris.2016070104.

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The use of data mining approaches in medicine and medical science has become necessary especially with the evolution of these approaches and their contributions medical decision support. Coronary artery disease (CAD) touches millions of people all over the world including a major portion in Algeria. However, much advancement has been done in medical science, but the early detection of CAD is still a challenge for prevention. Although, the early detection of CAD is a prevention challenge for clinicians. The subject of this paper is to propose new clinical decision support system (CDSS) for evaluating risk of CAD called CADSS. In this paper, the authors describe the characteristics of clinical decision support systems CDSSs for the diagnosis of CAD. The aim of this study is to explain the clinical contribution of CDSSs for medical decision-making and compare data mining techniques used for their implementation. Then, they describe their new fuzzy logic-based approach for detecting CAD at an early stage. Rules were extracted using a data mining technique and validated by experts, and the fuzzy expert system was used to handle the uncertainty present in the medical field. This work presents the main risk factors responsible for CAD and presents the designed CASS. The developed CADSS leads to 94.05% of accuracy, and its effectiveness was compared with different CDSS.

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Kumar, Praveen. "Universal Pulse Oximetry Screening for Early Detection of Critical Congenital Heart Disease." Clinical Medicine Insights: Pediatrics 10 (January 2016): CMPed.S33086. http://dx.doi.org/10.4137/cmped.s33086.

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Critical congenital heart disease (CCHD) is a major cause of infant death and morbidity worldwide. An early diagnosis and timely intervention can significantly reduce the likelihood of an adverse outcome. However, studies from the United States and other developed countries have shown that as many as 30%–50% of infants with CCHD are discharged after birth without being identified. This diagnostic gap is likely to be even higher in low-resource countries. Several large randomized trials have shown that the use of universal pulse-oximetry screening (POS) at the time of discharge from birth hospital can help in early diagnosis of these infants. The objective of this review is to share data to show that the use of POS for early detection of CCHD meets the criteria necessary for inclusion to the universal newborn screening panel and could be adopted worldwide.

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Iskandar, Aulia A., and Klaus Schilling. "Real-time Atrial Fibrillation Detection Using Artificial Neural Network on a Wearable Electrocardiogram." ICONIET PROCEEDING 2, no. 3 (February 13, 2019): 144–50. http://dx.doi.org/10.33555/iconiet.v2i3.26.

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Providing equal healthcare quality on heart diseases are an issue in developing countries, especially in Indonesia, due to is wide-spread areas. It is founded that the heart diseases occur not only in big cities but also in rural areas, that is caused by unhealthy lifestyle and foods. Heart disease itself is a disease with gradually symptoms changes that can be seen based on the hearts' electrical activity or electrocardiogram signals. Now, wearable medical devices are capable to be worn daily, so that, it can monitor our heart condition and alert if there is an abnormality. An embedded device worn on the chest can be used to perform a real-time data acquisition and processing of the electrocardiogram, that consists of a 1-lead ECG, an ARM processor, a Bluetooth module, an SD card, and rechargeable batteries. Also, by performing a digital filter and Tompkins algorithm, we obtain the P-wave presences and the heart rate variability values (heartbeat, average heartbeat, standard deviation, and root mean square) then by using an artificial neural network with 4 input, 6 hidden, and 1 output layers that has multi-layer perceptrons and backpropagation. We are able to perform a pre-diagnosis of atrial fibrillation, that is one of the common arrhythmias, from 41 recorded training samples (Physionet MIT/BIH AFDB and NSRDB) and 6 healthy subjects as test samples. The neural network has 0.1% error rate and needed 31548 epochs to train itself for classification the heart disease. Based on the results, this prototype can be used as a medical-grade wearable device thatcan help cardiologist in giving an early warning on the user's heart condition, so that it can prevent sudden death due to heart diseases in rural areas.

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G O, Apoorva, and Vishwas C N. "Machine Learning Based Heart Disease Prediction System." International Journal for Research in Applied Science and Engineering Technology 11, no. 2 (February 28, 2023): 1502–10. http://dx.doi.org/10.22214/ijraset.2023.49304.

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Abstract: Heart attack disease is one of the leading causes of the death worldwide. In today’s common modern life, deaths due to the heart disease had become one of major issues, that roughly one person lost his or her life per minute due to heart illness. Predicting the occurrence of disease at early stages is a major challenge nowadays. Machine learning when implemented in health care is capable of early and accurate detection of disease. In this work, the arising situations of Heart Disease illness are calculated. Datasets used have attributes of medical parameters. The datasets are been processed in python using ML Algorithm i.e., Random Forest Algorithm. This technique uses the past old patient records for getting prediction of new one at early stages preventing the loss of lives. In this work, reliable heart disease prediction system is implemented using strong Machine Learning algorithm which is the Random Forest algorithm, which read patient record data set in the form of CSV file. After accessing dataset the operation is performed and effective heart attack level is produced. Advantages of proposed system are High performance and accuracy rate and it is very flexible and high rates of success are achieved

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Letourneau, Karen M., David Horne, Reeni N. Soni, Keith R. McDonald, Fern C. Karlicki, and Randy R. Fransoo. "Advancing Prenatal Detection of Congenital Heart Disease: A Novel Screening Protocol Improves Early Diagnosis of Complex Congenital Heart Disease." Journal of Ultrasound in Medicine 37, no. 5 (October 13, 2017): 1073–79. http://dx.doi.org/10.1002/jum.14453.

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Bisgin, Pinar, Tom Strube, Jasmin Henze, Ingrid Ljungvall, Jens Häggström, Gerhard Wess, Julia Stadler, Christoph Schummer, Sven Meister, and Falk Maria Howar. "Thrilling AI – A novel, signal-based digital biomarker for diagnosing canine heart diseases." Current Directions in Biomedical Engineering 8, no. 2 (August 1, 2022): 765–68. http://dx.doi.org/10.1515/cdbme-2022-1195.

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Abstract Auscultation methods enable non-invasive diagnosis of diseases, e.g. of the heart, based on heartbeat sounds. Regular, early examinations using machine learning techniques could help to detect diseases at an early stage to prevent serious health conditions and then provide optimal therapy through continuous monitoring. There is already a lot of work on human data using AI algorithms to detect patterns in signals or images. However, there is hardly no work on detecting heart murmurs with digital such as Myxomatous Mitral Valve Disease. In this paper, we present a canine auscultation project that aims to provide a tool to establish a baseline of classification parameters from audio signals that could be used to monitor canine health status by analyzing deviations from this baseline. In the future, data analysis could also lead to prediction and early detection of other diseases.

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Gómez-Gutiérrez, René, Héctor Cruz-Camino, Consuelo Cantú-Reyna, Adrián Martínez-Cervantes, Diana Laura Vazquez-Cantu, Verónica Rivas-Soriano, Eduardo Vargas-Betancourt, and Cecilia Britton-Robles. "Early detection of and intervention for two newborns with critical congenital heart disease using a specialized device as part of a screening system." SAGE Open Medical Case Reports 8 (January 2020): 2050313X2092604. http://dx.doi.org/10.1177/2050313x20926041.

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Screening for critical congenital heart disease is a clinical method used for their early detection using pulse oximetry technology. This, followed by a diagnostic confirmatory protocol, allows timely therapeutic interventions that improve the newborn’s outcome. According to Mexican birth statistics, approximately 18,000–21,000 neonates are born with a form of congenital heart disease each year, of which 25% are estimated to be critical congenital heart disease. We report two cases with an early critical congenital heart disease detection and intervention through an innovative critical congenital heart disease screening program implemented in two Mexican hospitals. They integrated a new automated pulse oximetry data analysis method and a comprehensive follow-up system (Cárdi-k®). Both cases were confirmed by echocardiogram, which served for an intervention in the first week of life, and the patients were discharged in good clinical condition. In addition, to the routine physical assessments, the critical congenital heart disease screening program (which includes echocardiogram for presumptive positive cases) should be implemented in a timely manner.

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Alqahtani, Abdullah, Shtwai Alsubai, Mohemmed Sha, Lucia Vilcekova, and Talha Javed. "Cardiovascular Disease Detection using Ensemble Learning." Computational Intelligence and Neuroscience 2022 (August 16, 2022): 1–9. http://dx.doi.org/10.1155/2022/5267498.

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One of the most challenging tasks for clinicians is detecting symptoms of cardiovascular disease as earlier as possible. Many individuals worldwide die each year from cardiovascular disease. Since heart disease is a major concern, it must be dealt with timely. Multiple variables affecting health, such as excessive blood pressure, elevated cholesterol, an irregular pulse rate, and many more, make it challenging to diagnose cardiac disease. Thus, artificial intelligence can be useful in identifying and treating diseases early on. This paper proposes an ensemble-based approach that uses machine learning (ML) and deep learning (DL) models to predict a person’s likelihood of developing cardiovascular disease. We employ six classification algorithms to predict cardiovascular disease. Models are trained using a publicly available dataset of cardiovascular disease cases. We use random forest (RF) to extract important cardiovascular disease features. The experiment results demonstrate that the ML ensemble model achieves the best disease prediction accuracy of 88.70%.

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Gulfam Ahmad, Hafiz, and Muhammad Jasim Shah. "PREDICTION OF CARDIOVASCULAR DISEASES (CVDS) USING MACHINE LEARNING TECHNIQUES IN HEALTH CARE CENTERS." Azerbaijan Journal of High Performance Computing 4, no. 2 (December 31, 2021): 267–79. http://dx.doi.org/10.32010/26166127.2021.4.2.267.279.

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Cardiovascular Diseases (CVDs) are one of the most common health problems nowadays. Early diagnosis of heart disease is a significant concern for health professionals in medical centers. An incorrect forecast is more likely to have negative effects, such as disability or even death. Our research is motivated by the desire to predict cardiovascular diseases based on data mining that can be valuable to medical centers. Various data mining approaches are used for the early detection of cardiac diseases. This paper examines several research publications that work on various heart diseases. We compare and contrast several machine learning methods, such as KNN, ANN, Decision Tree, SVM, and Random Forest. We looked at 918 observations with several features related to heart disease. A comparative study with age and sex is established to predict cardiac disease using the decision tree approach. Our dataset contains 11 features that are used to forecast possible heart disease. One of the attributes indicates that the age factor has the most significant impact on heart disease. According to our findings, heart attacks cause four out of every five CVD deaths, with one-third of these deaths occurring suddenly in those under 70.

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Asmare, Melkamu Hunegnaw, Benjamin Filtjens, Frehiwot Woldehanna, Luc Janssens, and Bart Vanrumste. "Rheumatic Heart Disease Screening Based on Phonocardiogram." Sensors 21, no. 19 (September 30, 2021): 6558. http://dx.doi.org/10.3390/s21196558.

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Rheumatic heart disease (RHD) is one of the most common causes of cardiovascular complications in developing countries. It is a heart valve disease that typically affects children. Impaired heart valves stop functioning properly, resulting in a turbulent blood flow within the heart known as a murmur. This murmur can be detected by cardiac auscultation. However, the specificity and sensitivity of manual auscultation were reported to be low. The other alternative is echocardiography, which is costly and requires a highly qualified physician. Given the disease’s current high prevalence rate (the latest reported rate in the study area (Ethiopia) was 5.65%), there is a pressing need for early detection of the disease through mass screening programs. This paper proposes an automated RHD screening approach using machine learning that can be used by non-medically trained persons outside of a clinical setting. Heart sound data was collected from 124 persons with RHD (PwRHD) and 46 healthy controls (HC) in Ethiopia with an additional 81 HC records from an open-access dataset. Thirty-one distinct features were extracted to correctly represent RHD. A support vector machine (SVM) classifier was evaluated using two nested cross-validation approaches to quantitatively assess the generalization of the system to previously unseen subjects. For regular nested 10-fold cross-validation, an f1-score of 96.0 ± 0.9%, recall 95.8 ± 1.5%, precision 96.2 ± 0.6% and a specificity of 96.0 ± 0.6% were achieved. In the imbalanced nested cross-validation at a prevalence rate of 5%, it achieved an f1-score of 72.2 ± 0.8%, recall 92.3 ± 0.4%, precision 59.2 ± 3.6%, and a specificity of 94.8 ± 0.6%. In screening tasks where the prevalence of the disease is small, recall is more important than precision. The findings are encouraging, and the proposed screening tool can be inexpensive, easy to deploy, and has an excellent detection rate. As a result, it has the potential for mass screening and early detection of RHD in developing countries.

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Journal articles on the topic 'Early detection of heart disease'

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