Relevant bibliographies by topics / Early detection of heart disease

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Early detection of heart disease'

Author: Grafiati
Published: 12 May 2023
Last updated: 31 July 2025

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

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Xaitov, Farxod Nasriddin o'g'li., and Zilola Mo'min qizi. Axmatjonova. "EARLY DETECTION AND DIAGNOSIS OF HEART DISEASES." Multidisciplinary Journal of Science and Technology 5, no. 1 (2025): 33–37. https://doi.org/10.5281/zenodo.14621680.

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The article discusses factors that influence the development of the disease, such as unhealthy lifestyle, stress, genetic factors and aspects related to chronic diseases. Early detection and diagnosis of heart disease. Heart disease is the leading cause of death and disability worldwide. This article discusses the importance of early detection and diagnosis of heart disease. Early detection of heart disease can save patients' lives and prevent complications. The article focuses on the early signs of heart disease, risk factors, and diagnostic methods, including modern methods such as electroca
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Dr., Deepak NR, Prasad Ashansh, BE Harsh, B. Shurthi, Moond Himanshu, and Sharma Chekit. "Machine Learning for Early Detection of Heart Disease." Journal of Research and Review: Hacking Techniques and Information Security Systems 1, no. 1 (2025): 33–38. https://doi.org/10.5281/zenodo.15195374.

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<em>Heart disease continues to be a leading cause of death globally, with early detection playing a critical role in improving outcomes and preventing severe complications. Traditional diagnostic methods, such as electrocardiograms (ECGs), echocardiograms, and stress tests, often detect conditions at later stages, when intervention becomes more challenging. This report explores the role of machine learning in early heart disease detection, highlighting various ML techniques such as supervised learning, deep learning, and unsupervised learning. These models can identify patterns and correlation
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Sobeih, Abul Ahmed. "HEART HEALTH CHECK: SCREENING UNIVERSITY STUDENTS FOR RHEUMATIC HEART DISEASE." International Journal of Prevention Practice and Research 04, no. 03 (2024): 01–07. http://dx.doi.org/10.55640/medscience-abcd634.

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This study examines the feasibility and effectiveness of screening university students for Rheumatic Heart Disease (RHD) as part of a broader public health initiative. RHD remains a significant cause of morbidity and mortality, particularly in resource-limited settings, despite being largely preventable. The screening program targets university students due to their age group's susceptibility to RHD and the potential to intervene early in the disease progression. Through a combination of clinical assessments, echocardiography, and laboratory tests, students are evaluated for signs and symptoms
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Aleem, Anuoluwapo Oluwayemisi. "MACHINE LEARNING FOR EARLY HEART DISEASE DETECTION." International Research Journal of Modernization in Engineering Technology and Science 06, no. 09/September-2024 (2024): 1–14. https://doi.org/10.56726/IRJMETS61879.

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Heart disease has been proven to be the leading cause of death for both men and women, killing about 697,000&nbsp;people in the US in 2020, which makes it a major concern to be dealt with. Identifying heart disease in patients&nbsp;could be quite challenging due to several contributory risk factors, which requires some high level of&nbsp;techniques. Machine learning proves to be effective in predicting heart disease in patients given the&nbsp;contributory risk factors. In this study, we used seven different machine learning algorithms such as K-Nearest&nbsp;Neighbors, support vector machine, D
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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 r
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Wonderful, Ntepa. "Heart disease detection system." i-manager's Journal on Data Science & Big Data Analytics 2, no. 1 (2024): 1. http://dx.doi.org/10.26634/jds.2.1.20688.

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This paper presents a comprehensive exploration into the utilization of machine learning (ML) techniques to revolutionize medical diagnostics, with a specific focus on enhancing the detection of heart disease. Recognizing the imperative need for early diagnosis to address the global prevalence of heart disease, this study delves into the development and application of advanced ML principles. The paper aims to construct a robust ML model capable of analyzing diverse patient data sets, including electronic health records and genetic information, to discern intricate patterns and correlations imp
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Kumar, Prof Amit, Harshika Bansal, Ayush Jaiswal, and Sovit Kumar Gupta. "Early Disease Prediction using Ml." International Journal of Advanced Engineering and Nano Technology 10, no. 11 (2023): 1–4. http://dx.doi.org/10.35940/ijaent.i9694.11101123.

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The approach employed in disease prediction using machine learning involves making forecasts about various diseases by utilizing symptoms provided by patients or other individuals. The supervised machine learning approaches called random forest classifier, KNN classifier, SVMs classifier are employed to forecast the disease. These algorithms are used to determine the disease's probability. Accurate medical data analysis helps with patient care and early disease identification as biomedical and healthcare data volumes rise. Diabetes, heart diseases are just a few of the illnesses we can forecas
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Dr., Chandrasekar Vadivelraju, N. Sughanditha Reddy Duttala, Praneeth Kumar Gowd Korrapati, Vaahini Katakaraju, and Suresh Pujari. "Heart Disease Prediction." Heart Disease Prediction 9, no. 1 (2024): 6. https://doi.org/10.5281/zenodo.10527852.

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The increasing breakthroughs in illness diagnosis classification and identification systems have led to a steady growth in the incorporation of machine learning in medical diagnostics. These systems provide crucial data aiding medical professionals in the early detection of fatal diseases, significantly enhancing patient survival rates. Globally, heart disease stands as the leading cause of death. The escalating rates of heart strokes among juveniles underscore the need for an early detection system to prevent potential incidents. Frequent and costly tests like electrocardiograms (ECG) are imp
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Singh, Ankita, and Nupur Soni. "Detection of Cardiovascular Disease Using AI." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 12 (2024): 1–5. https://doi.org/10.55041/ijsrem40092.

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Detecting illnesses in their early stages can help prevent serious complications and improve treatment outcomes. Early diagnosis is critical because it reduces the risk of severe consequences, as prevention is better than cure. A high death rate often occurs when diseases are not detected early. Expert systems can bridge this gap by diagnosing diseases automatically in their initial phases. These systems use fuzzy, rule-based engines to analyze patient data and apply forward-chaining techniques for diagnosis. In this study, data such as age, gender, blood sugar levels, blood pressure, and ECG
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Manzar, Shabih. "Fetal Atrial Flutter: Early Detection and Treatment." Obstetrics Gynecology and Reproductive Sciences 7, no. 5 (2023): 01–03. http://dx.doi.org/10.31579/2578-8965/176.

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Fetal atrial flutter (AF) can occur with structurally normal hearts or congenital heart disease, including atrioventricular septal defect, hypoplastic left heart syndrome, pulmonary atresia, and Ebstein's malformation. 1 It is important to detect fetal AF early and treat it appropriately. We present a case of fetal AF that was treated timely, resulting in no associated complications. The infant was born by cesarean section at a gestational age of 37 weeks with an Apgar score of 9 and 9. The birth weight was 2860 grams. The antenatal history was positive for fetal tachycardia.
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Dissertations / Theses on the topic "Early detection of heart disease"

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Hartnick, Maria Diana. "Echocardiography for early detection of heart disease in high risk diabetic patients." Thesis, Cape Peninsula University of Technology, 2015. http://hdl.handle.net/20.500.11838/1566.

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Masters of Technology: Radiography in the Faculty of Health and Wellness Sciences at the Cape Peninsula University of Technology 2015<br>Introduction: Diabetes mellitus is a chronic disease with a significant impact on personal lifestyle and wellbeing. It is associated with a high prevalence of myocardial disease, the early detection of which is important for prevention of disease progression. Although echocardiography is recognised as a leading cardiovascular imaging modality, there has been limited work on its role in the early detection of diabetes-related myocardial dysfunction. Th
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Kounali, Daphne. "Early growth and coronary heart disease." Thesis, University of Southampton, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.436926.

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Springer, David Brian. "Mobile phone-based rheumatic heart disease detection." Thesis, University of Oxford, 2015. https://ora.ox.ac.uk/objects/uuid:5ec8c818-dafb-4571-8198-97607f8d0451.

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Rheumatic heart disease (RHD), the permanent damage of the heart valves caused by an untreated 'strep throat' infection, is the leading cause of cardiovascular mortality and morbidity in children and young adults worldwide. Simple penicillin treatment after the early diagnosis of RHD can stop recurring bouts of the condition, which lead to the most severe valvulopathy, and ultimately, heart failure. However, RHD is an under-diagnosed condition in the developing world, as such a diagnosis requires, at a minimum, a trained clinician to perform auscultation to detect pathological heart sounds. Tr
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Bull, Adrian Richard. "Early determinants of blood pressure and related disease." Thesis, University of Southampton, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.238962.

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Suh, Doug Young. "Knowledge-based boundary detection system : on MRI cardiac image sequences." Diss., Georgia Institute of Technology, 1990. http://hdl.handle.net/1853/13291.

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Pursiainen, V. (Ville). "Autonomic dysfunction in early and advanced Parkinson's disease." Doctoral thesis, University of Oulu, 2007. http://urn.fi/urn:isbn:9789514283888.

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Abstract Parkinson's disease (PD) is known to affect both the extrapyramidal system and the autonomic nervous system even in the early phases of the disease. This study was designed to evaluate cardiovascular autonomic regulation in early PD by measuring heart rate (HR) variability from 24-hour ECG recordings. The dynamics of blood pressure (BP), HR and sweating in patients with and without wearing-off were assessed during clinical observations after a morning dose of levodopa. In patients with wearing-off the tests were repeated after selegiline withdrawal. The power spectral components of H
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Shen, Ze-ping. "An application of neural networks for the detection of coronary heart disease." Thesis, Brunel University, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.385186.

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Heard, Stephanie. "Plant pathogen sensing for early disease control." Thesis, University of Manchester, 2014. https://www.research.manchester.ac.uk/portal/en/theses/plant-pathogen-sensing-for-early-disease-control(48949f80-2596-4ce2-912a-6513e72f6a8d).html.

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Sclerotinia sclerotiorum, a fungal pathogen of over 400 plant species has been estimated to cost UK based farmers approximately £20 million per year during severe outbreak (Oerke and Dehne 2004). S. sclerotiorum disease incidence is difficult to predict as outbreaks are often sporadic. Ascospores released from the fruiting bodies or apothecia can be dispersed for tens of kilometres. This makes disease control problematic and with no S. sclerotiorum resistant varieties available, growers are forced to spray fungicides up to three times per flowering season in anticipation of the arrival of this
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Mendel, Julian L. "Laurel Wilt Disease: Early Detection through Canine Olfaction and "Omics" Insights into Disease Progression." FIU Digital Commons, 2017. http://digitalcommons.fiu.edu/etd/3475.

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Laurel wilt disease is a vascular wilt affecting the xylem and water conductivity in trees belonging to the family Lauraceae. The disease was introduced by an invasive species of ambrosia beetle, Xyleborus glabratus. The beetle, together with its newly described fungal symbiont Raffaelea lauricola (pathogenic to host trees), has lead to the devastation and destruction of over 300 million wild redbay trees in southeastern forests. Ambrosia beetles make up a very unique clade of beetle and share a co-evolved obligatory mutualistic relationship with their partner fungi. Rather than consuming host
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Balderson, Diane E. "Observations on the detection of ventricular late potentials." Thesis, Queen's University Belfast, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.238982.

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Books on the topic "Early detection of heart disease"

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Carlos, Kaski Juan, and Holt David W, eds. Myocardial damage: Early detection by novel biochemical markers. Kluwer Academic, 1998.

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Policy, Toronto Working Group on Cholesterol. Detection and management of asymptomatic hypercholesterolemia: A policy document. s.n., 1989.

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Shen, Ze-ping. An application of neural networks for the detection of coronary heart disease. Brunel University, 1994.

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McCarthy, Joseph C. Early hip disorders: Advances in detection and minimally invasive treatment. Springer, 2011.

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name, No. Early hip disorders: Advances in detection and minimally invasive treatment. Springer, 2003.

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editor, Mordini E. (Emilio), and Green Manfred editor, eds. Internet-based intelligence in public health emergencies: Early detection and response in disease outbreak crises. IOS Press, 2011.

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Rosen, Shara. Trends in the early diagnosis of cardiovascular disease: Worldwide market opportunities. Kalorama Information, 2001.

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Fitzgerald, Rebecca C. Pre-invasive disease: Pathogenesis and clinical management. Springer, 2011.

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Patlak, Margie. Mammography and beyond: Developing technologies for the early detection of breast cancer : a non-technical summary. Edited by National Cancer Policy Board (U.S.). Committee on the Early Detection of Breast Cancer and National Research Council (U.S.). Commission on Life Sciences. National Academy Press, 2001.

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Wells, F. O. Preventive medicine: Working for patients : a review of the therapeutic areas where screening programmes and the early detection and treatment of disease will benefit patients and the NHS. Association of the British Pharmaceutical Industry, 1989.

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Book chapters on the topic "Early detection of heart disease"

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Gupta, Priyanka, and D. D. Seth. "Early Detection of Heart Disease Using Multilayer Perceptron." In Micro-Electronics and Telecommunication Engineering. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-9512-5_28.

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Forbes, Malcolm P., and Harris A. Eyre. "Screening for Depression in Coronary Heart Disease: Detection of Early Disease States." In Cardiovascular Diseases and Depression. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-32480-7_28.

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Renju, R. S., and P. S. Deepthi. "Early Detection of Heart Disease Using Feature Selection and Classification Techniques." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3481-2_17.

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Chen, Tzu-Chia. "Optimized Support Vector Machine for Early and Accurate Heart Disease Detection." In Advancements in Science and Technology for Healthcare, Agriculture, and Environmental Sustainability. CRC Press, 2024. http://dx.doi.org/10.1201/9781032708348-5.

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Mishra, Sarita, Manjusha Pandey, Siddharth Swarup Rautaray, and Mahendra Kumar Gourisaria. "A Proposal for Early Detection of Heart Disease Using a Classification Model." In Communications in Computer and Information Science. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1480-4_32.

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Sowjanya, K. Krishna, and K. P. Bindu Madavi. "Preventive Health Care System for Early Heart Disease Detection Using IoT and Machine Learning." In Information Systems Engineering and Management. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-65022-2_10.

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Agu, Sunday Clement, Uchenna Kenneth Ezemagu, and Olivér Hornyák. "Biomedical Informatics: Considering Predictive Models for Early Detection of Heart Diseases." In Lecture Notes in Networks and Systems. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-81685-7_9.

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Feightner, John, and Graham Worrall. "Early Detection of Depression." In Preventing Disease. Springer New York, 1990. http://dx.doi.org/10.1007/978-1-4612-3280-3_15.

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Wallace, Deborah, and Rodrick Wallace. "Early Mortality from Ischemic Heart Disease (Coronary Heart Disease)." In Right-to-Work Laws and the Crumbling of American Public Health. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-72784-4_6.

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Macakaite, Karina, and Arjab Singh Khuman. "Risk Detection of Heart Disease." In Artificial Intelligence in Healthcare. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-5272-2_14.

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Conference papers on the topic "Early detection of heart disease"

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Gunawan, Kemas Rahmat Saleh Wiharja, and Hasmawati. "Early Detection of Heart Disease with Graph Neural Network." In 2024 12th International Conference on Information and Communication Technology (ICoICT). IEEE, 2024. http://dx.doi.org/10.1109/icoict61617.2024.10698473.

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J, Thirunavukkarasu, Dhivya Dharshini J, Parthiban M, Hemamaalani S. J, Janavi Thiyagarajan, and Kirthana Mohan R. "CardioSense-A Deep Learning Enhanced Early Heart Disease Detection." In 2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES). IEEE, 2024. https://doi.org/10.1109/icses63760.2024.10910329.

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Rofiudin, Ahmad Sidik, Tien Febrianti Kusumasari, and Sinung Suakanto. "Backend of Heart Rate Detection System with Early Warning for Patients with Heart Disease." In 2024 International Conference on ICT for Smart Society (ICISS). IEEE, 2024. http://dx.doi.org/10.1109/iciss62896.2024.10750959.

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Pande, Pooja Kuldeep, Prashant Khobragade, Samir N. Ajani, and Vaibhav P. Uplanchiwar. "Early Detection and Prediction of Heart Disease with Machine Learning Techniques." In 2024 International Conference on Innovations and Challenges in Emerging Technologies (ICICET). IEEE, 2024. http://dx.doi.org/10.1109/icicet59348.2024.10616294.

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Riyadi, Slamet, Fitri Alfiana, Muhammad Iqbal Al-Habib, Cahya Damarjati, and Erika Loniza. "Early Detection of Heart Disease on Imbalanced Data Using Gradient Boosting." In 2024 Ninth International Conference on Informatics and Computing (ICIC). IEEE, 2024. https://doi.org/10.1109/icic64337.2024.10956329.

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Amareshwari, N. Radhika, Saravanan Matheswaran, R. Anand, and V. Amirthalingam. "Predictive Cardiology: A CNN-LSTM Attention Framework for Early Heart Disease Detection." In 2024 International Conference on Emerging Research in Computational Science (ICERCS). IEEE, 2024. https://doi.org/10.1109/icercs63125.2024.10894773.

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Al-Sheyab, Asalla M., Msallam Kousa, Hanadi Bani Hamad, Adai Almomani, and Ali Elrashidi. "Machine Learning Approaches for Early Detection of Heart Diseases." In 2024 25th International Arab Conference on Information Technology (ACIT). IEEE, 2024. https://doi.org/10.1109/acit62805.2024.10877043.

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Dankan Gowda, V., Annepu Arudra, K. M. Mouna, Sanjog Thapa, Vaishali N. Agme, and KDV Prasad. "Predictive Performance and Clinical Implications of Machine Learning in Early Coronary Heart Disease Detection." In 2024 2nd World Conference on Communication & Computing (WCONF). IEEE, 2024. http://dx.doi.org/10.1109/wconf61366.2024.10691973.

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Riyadi, Slamet, Muhammad Iqbal Al-Habib, Fitri Alfiana, Cahya Damarjati, Yessi Jusman, and Suzaimah Ramli. "Early Detection of Coronary Heart Disease Using Exploration Data Analysis and Gradient Boosting Method." In 2024 International Conference on Information Technology and Computing (ICITCOM). IEEE, 2024. https://doi.org/10.1109/icitcom62788.2024.10762294.

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Ingole, Balaji Shesharao, Vishnu Ramineni, Vivekananda Jayaram, et al. "Prediction and Early Detection of Heart Disease: A Hybrid Neural Network and SVM Approach." In 2024 IEEE 17th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC). IEEE, 2024. https://doi.org/10.1109/mcsoc64144.2024.00054.

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Reports on the topic "Early detection of heart disease"

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Wu, Meiye, Ryan Wesley Davis, and Anson Hatch. Portable microfluidic raman system for rapid, label-free early disease signature detection. Office of Scientific and Technical Information (OSTI), 2015. http://dx.doi.org/10.2172/1222536.

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Rostaminejad, Marzieh. Early Diagnosis of Alzheimer's disease using Electrochemical-based Nanobiosensors for miRNA Detection. Peeref, 2022. http://dx.doi.org/10.54985/peeref.2207p6024343.

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Cohen, Yuval, Christopher A. Cullis, and Uri Lavi. Molecular Analyses of Soma-clonal Variation in Date Palm and Banana for Early Identification and Control of Off-types Generation. United States Department of Agriculture, 2010. http://dx.doi.org/10.32747/2010.7592124.bard.

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Date palm (Phoenix dactylifera L.) is the major fruit tree grown in arid areas in the Middle East and North Africa. In the last century, dates were introduced to new regions including the USA. Date palms are traditionally propagated through offshoots. Expansion of modern date palm groves led to the development of Tissue Culture propagation methods that generate a large number of homogenous plants, have no seasonal effect on plant source and provide tools to fight the expansion of date pests and diseases. The disadvantage of this procedure is the occurrence of off-type trees which differ from t
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Deshpande, Alina. RED Alert – Early warning or detection of global re-emerging infectious disease (RED). Office of Scientific and Technical Information (OSTI), 2016. http://dx.doi.org/10.2172/1261795.

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Tang, Xiangyang. Early Detection of Amyloid Plaque in Alzheimer's Disease via X-Ray Phase CT. Defense Technical Information Center, 2014. http://dx.doi.org/10.21236/ada612057.

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Tang, Xiangyang. Early Detection of Amyloid Plaque in Alzheimer's Disease via X-Ray Phase CT. Defense Technical Information Center, 2013. http://dx.doi.org/10.21236/ada582946.

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Tang, Xiangyang. Early Detection of Amyloid Plaque in Alzheimer's Disease Via X-ray Phase CT. Defense Technical Information Center, 2015. http://dx.doi.org/10.21236/ada620373.

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Li, Jiangwei. Applications of a single-molecule detection in early disease diagnosis and enzymatic reaction study. Office of Scientific and Technical Information (OSTI), 2008. http://dx.doi.org/10.2172/964365.

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López-Cuenca, Inés, Rubén Masa-Castro, Yael Hoz-Ruiz, et al. Tears as a Window to Alzheimer's Disease: A Systematic Re-view on Biomarkers for Early Detection. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2024. https://doi.org/10.37766/inplasy2024.12.0034.

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Sriuranpong, Virote. The role of SHP-1 promoter 2 hypermethylation detection of lymph node micrometastasis in resectable nonmetastasis NSCLC as a prognostic marker of disease. Chulalongkorn University, 2010. https://doi.org/10.58837/chula.res.2010.16.

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Introduction; Despite adequate surgical management of stage I non-small cell lung cancer, many patients still have relapsed of disease which leads to mortality. Micrometastasis of tumor is the postulate mechanism which might not be detected by standard H&amp;E method. The author conducted the study of epithelial methylation marker, SHP-1 Promoter 2 (SHP1P2) methylation as a potential molecular marker to detect high risk relapsed of disease in stage I resectable non-small cell lung cancer (NSCLC). Method; To explore the potential role of SHP1P2 methylation to detect micrometastasis, Lymph node
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