Relevant bibliographies by topics / Heart Attack prediction

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

Academic literature on the topic 'Heart Attack prediction'

Author: Grafiati
Published: 4 June 2025
Last updated: 15 July 2025

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Journal articles on the topic "Heart Attack prediction"

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Yadav, Suraj Dineshkumar. "Heart Attack Prediction." International Journal for Research in Applied Science and Engineering Technology 12, no. 12 (2024): 2081–89. https://doi.org/10.22214/ijraset.2024.66163.

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Cardiovascular diseases, including heart attacks, remain a leading cause of mortality globally. Early prediction and intervention play a critical role in preventing and managing such conditions. This project presents a comprehensive approach to heart attack prediction through the integration of machine learning techniques and a user-friendly graphical user interface (GUI) implemented in Python. The system leverages a dataset of relevant health parameters, including age, blood pressure, cholesterol levels, and other key factors. A machine learning model, trained on historical data, is employed
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Nandal, Neha, Lipika Goel, and ROHIT TANWAR. "Machine learning-based heart attack prediction: A symptomatic heart attack prediction method and exploratory analysis." F1000Research 11 (September 29, 2022): 1126. http://dx.doi.org/10.12688/f1000research.123776.1.

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Background; Heart attack prediction is one of the serious causes of morbidity in the world’s population. The clinical data analysis includes a very crucial disease i.e., cardiovascular disease as one of the most important sections for the prediction. Data Science and machine learning (ML) can be very helpful in the prediction of heart attacks in which different risk factors like high blood pressure, high cholesterol, abnormal pulse rate, diabetes, etc... can be considered. The objective of this study is to optimize the prediction of heart disease using ML. Methods: In this paper, we are presen
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D.Swetha, V.Priyanka, Ganga Sushma V., L.Bhuvaneshwar, and Kagolla Sivachandra. "Heart Attack Prediction using Machine Learning." Research and Applications: Emerging Technologies 6, no. 3 (2024): 29–37. https://doi.org/10.5281/zenodo.12663004.

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<em>Heart attack prediction is one in every of the real causes of horribleness inside the world&rsquo;s populace. The clinical records evaluation includes a particularly important disorder i.e., cardiovascular disease as one of the maximum important segments for the prediction. data science and machine learning (ML) can be surprisingly supportive within the prediction of coronary heart attacks in which numerous hazard additives like excessive blood pressure, high ldl cholesterol, abnormal pulse rate, diabetes, etc... can be considered. The objective of this study is to optimize the prediction
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Chamidah, Nur, Budi Lestari, Hendri Susilo, Mochamad Yusuf Alsagaff, I. Nyoman Budiantara, and Dursun Aydin. "Spline Estimator in Nonparametric Ordinal Logistic Regression Model for Predicting Heart Attack Risk." Symmetry 16, no. 11 (2024): 1440. http://dx.doi.org/10.3390/sym16111440.

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In Indonesia, one of the main causes of death for both young and elderly people is heart attacks, and the main cause of heart attacks is non-communicable diseases such as hypertension. Deaths due to heart attacks caused by non-communicable diseases, namely hypertension, rank first in Indonesia. Therefore, predictions of the risk of having a heart attack caused by hypertension need serious attention. Further, for determining whether a patient is experiencing a heart attack, an effective method of prediction is required. One efficient approach is to use statistical models. This study discusses p
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Romeo Jousef A. Laxamana. "Heart Attack Prediction using Machine Learning Algorithms." Journal of Electrical Systems 20, no. 5s (2024): 1428–36. http://dx.doi.org/10.52783/jes.2474.

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One of the most crucial parts of the human body is the heart. When the heart's blood supply is cut off, a heart attack happens. The most frequent cause of a blockage is the buildup of fats, cholesterol, and other substances inside the coronary arteries that provide blood to the heart (which eventually results in plaque growth). This study sought to identify which anthropometric characteristics had a high likelihood of having an impact on a person having a heart attack in order to design a program for heart attack analysis using machine learning algorithms. The researchers were able to acquire
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A, Balaji. "A Hybrid Intelligent System Framework for the Prediction of Heart Disease Using Machine Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem32164.

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In today’s modern world cardiovascular disease is the most lethal one. This disease attacks a person instantly that might create unexpected consequences for the human life. So diagnosing patients correctly on time is the most challenging task for the medical fraternity. The heart disease treatment is quite high and not affordable by most of the patients particularly in India. The research scope is to develop an early prediction treatment using data mining technologies. Now a day every hospital keeps the periodical medical reports of cardiovascular patients through some hospital management syst
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Firizkiansah, Angge, Imron Rizki Maulana, Ali Muhammad, and Aliyah Kurniasih. "Optimization of Supervised Learning Algorithms for Early Prediction of Heart Attack Risk." Journal of Artificial Intelligence and Engineering Applications (JAIEA) 4, no. 3 (2025): 1797–802. https://doi.org/10.59934/jaiea.v4i3.1020.

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Cardiovascular disease, particularly heart attacks, persists as a primary global cause of mortality. Heart attacks arise from an abrupt obstruction of oxygenated blood flow to a segment of the cardiac muscle, resulting in inadequate oxygen supply to the heart. This obstruction may stem from modifiable risk factors, including suboptimal dietary habits, physical inactivity, obesity, and tobacco consumption, alongside non-modifiable factors such as age, sex, and familial predisposition. Contemporary research increasingly focuses on preemptive strategies against heart attacks to mitigate associate
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Yadav, M. Sai Kiran. "Heart Attack Prediction Using Arduino." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (2021): 1558–61. http://dx.doi.org/10.22214/ijraset.2021.36427.

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Nowadays several folks are mislaying their lives because of coronary heart assault and absence of clinical interest to affected person at accurate level. Hence, on this mission we're imposing coronary heart assault prediction device with the use of Arduino. The first level of coronary heart assault is surprising change in coronary heart beat. Suddenly it is going too excessive or too low. At this level we predict the coronary heart assault by the means of an Arduino and a coronary heart beat sensor, then will inject drug into the body, by means of controlling vale or pump which can be position
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Shi, Tianyu. "The Research about Heart Attack Prediction Model." Highlights in Science, Engineering and Technology 99 (June 18, 2024): 28–33. http://dx.doi.org/10.54097/xzaanz17.

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Nowadays, coronary heart disease is becoming the most important cause of death all over the world. The use of science technology makes it much more easier for people to analyze the causes for the different kinds of diseases. This article uses the Behavioral Risk Factor Surveillance System to assemble data on health-related risk behaviors from more than 400,000 Americans and analyze these data to provide a precise prediction model for coronary heart disease. The article uses methods like Logistic regression, support vector machine (SVM) and random forest (RF) to explore a better prediction mode
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Dr., G. Jayamurugan. "Predicting Heart Attack." International Research Journal of Computer Science 10, no. 06 (2023): 330–32. http://dx.doi.org/10.26562/irjcs.2023.v1006.10.

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Heart diseases are increasing day by day, and predicting these diseases is very important and worrying. This diagnosis is a difficult task because it must be done accurately and efficiently. Most studies focus on patients at risk for heart disease based on a variety of medical conditions. We proposed a heart disease predictor that uses the patient's medical history to predict whether a patient will be diagnosed with heart disease. We use various machine learning algorithms, such as logistic regression and KNN, to predict and classify heart patients. A useful way to formalize how the model can
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Dissertations / Theses on the topic "Heart Attack prediction"

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Fernandes, Maria Inês Resende da Lomba. "Data Mining Application for Healthcare Sector: Predictive Analysis of Heart Attacks." Master's thesis, 2021. http://hdl.handle.net/10362/127475.

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Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business Intelligence<br>Cardiovascular diseases are the main cause of the number of deaths in the world, being the heart disease the most killing one affecting more than 75% of individuals living in countries of low and middle earnings. Considering all the consequences, firstly for the individual’s health, but also for the health system and the cost of healthcare (for instance, treatments and medication), specifically for cardiovascular di
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Books on the topic "Heart Attack prediction"

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United States. National Aeronautics and Space Administration., ed. Aerothermodynamic measurement and prediction for modified orbiter at Mach 6 and 10. American Institute of Aeronautics and Astronautics, 1995.

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D, Gunnar Sevelius M. An Untold Medical Story, Coronary Blood Flow, Heart Attack Prediction, Prevention and Treatment. AuthorHouse, 2011.

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Book chapters on the topic "Heart Attack prediction"

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Kumar Das, Ashish, Ravi Vishwakarma, Adarsh Kumar Bharti, and Jyoti Singh Kirar. "Heart attack analysis and prediction system." In Computational Intelligence Aided Systems for Healthcare Domain. CRC Press, 2023. http://dx.doi.org/10.1201/9781003368342-11.

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Akalya, A., and V. Swedha. "Heart Attack Prediction Using Big Data Analytics." In IFIP Advances in Information and Communication Technology. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-69986-3_22.

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Balabaeva, Ksenia, and Sergey Kovalchuk. "Neural Additive Models for Explainable Heart Attack Prediction." In Computational Science – ICCS 2022. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08757-8_11.

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Gour, Sanjay, Punita Panwar, Divya Dwivedi, and Chetan Mali. "A Machine Learning Approach for Heart Attack Prediction." In Intelligent Sustainable Systems. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-6309-3_70.

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Aishwarya, N., D. Yathishan, R. Alageswaran, and D. Manivannan. "AutoML Based IoT Application for Heart Attack Risk Prediction." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-5994-5_3.

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Raihan, M., Md Nazmos Sakib, Sk Nizam Uddin, Md Arin Islam Omio, Saikat Mondal, and Arun More. "Smartphone-Based Heart Attack Prediction Using Artificial Neural Network." In Algorithms for Intelligent Systems. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0586-4_22.

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Prakash, Shubham, Saswati Mahapatra, and Mamata Nayak. "Data Analysis in Clinical Decision Making—Prediction of Heart Attack." In Smart Innovation, Systems and Technologies. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-6068-0_33.

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Yadav, Dinesh Kumar, Bhawna Kaushik, Anil Thakur, Isha, Chetna Vaid Kwatra, and Harpreet Kaur. "A review of heart attack prediction using deep learning strategies." In Advances in Electronics, Computer, Physical and Chemical Sciences. CRC Press, 2025. https://doi.org/10.1201/9781003616252-77.

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Ghosh, Pinaki, Umesh Kumar Lilhore, Sarita Simaiya, Atul Garg, Devendra Prasad, and Ajay Kumar. "Prediction of the Risk of Heart Attack Using Machine Learning Techniques." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-4687-5_47.

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Deveci, Furkan, and Mete Yağanoğlu. "Innovative Feature Engineering for Enhanced Heart Attack Prediction: A Novel Approach." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-3358-6_33.

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Conference papers on the topic "Heart Attack prediction"

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Mali, Saurabh, and Karthika Veeramani. "Heart Attack Prediction Using Ensemble Learning." In 2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT). IEEE, 2024. http://dx.doi.org/10.1109/iconscept61884.2024.10627780.

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Mall, Shubham. "Heart Attack Prediction using Machine Learning Techniques." In 2024 4th International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE). IEEE, 2024. http://dx.doi.org/10.1109/icacite60783.2024.10617300.

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Kumar, Manoj, Dogga Pavan Sekhar, Kesanakurthi Naga Siddhartha, Urmila Pilania, Ketha Sathwik Reddy, and Alasyam Shashank. "Heart Attack Prediction Using Machine Learning Algorithms." In 2024 International Conference on Cybernation and Computation (CYBERCOM). IEEE, 2024. https://doi.org/10.1109/cybercom63683.2024.10803124.

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Bonthu, Yasaswini, Subbarao Mannam, Gayithri Kandikunta, Vikranth Goud Keshagani, and Greeshma Sarath. "Heart Attack Risk Prediction Using Advanced Machine Learning Techniques." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10725867.

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N, Janaraniani, Divya P, Madhukiruba E, R. Santhosh, R. Reshma, and D. Selvapandian. "Expression of Concern for: Heart Attack Prediction using Machine Learning." In 2022 4th International Conference on Inventive Research in Computing Applications (ICIRCA). IEEE, 2022. http://dx.doi.org/10.1109/icirca54612.2022.10703503.

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Saratkar, Saniya, Aarti Chaudhari, Trupti Thute, Rohini Raut, Gayatri Thakre, and Hemant Kumar. "Assessment of Heart-Attack Prediction using Fuzzy Rule Based System." In 2024 8th International Conference on Computing, Communication, Control and Automation (ICCUBEA). IEEE, 2024. https://doi.org/10.1109/iccubea61740.2024.10774808.

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Vandana, CP, M. Shivani Kashyap, D. Yashas, and Neha Singh. "Exploratory Analysis of Heart Attack and Breast Cancer Early Stage Prediction." In 2024 Third International Conference on Trends in Electrical, Electronics, and Computer Engineering (TEECCON). IEEE, 2024. https://doi.org/10.1109/teeccon64024.2024.10941411.

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Micheal, A. Ancy, Kriti Gupta, Rishi Singh, Vikram Singh, and Bipasha Mohanty. "HBTrackr: AI-Based Heart Attack Prediction through ECG Monitoring on Wearable Devices." In 2024 First International Conference on Pioneering Developments in Computer Science & Digital Technologies (IC2SDT). IEEE, 2024. http://dx.doi.org/10.1109/ic2sdt62152.2024.10696496.

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Nawar, Abbas Khalifa, Sabah Abdulazeez Jebur, Hasan Abdulrazzaq Jawad, Mothefer Majeed Jahefer, Lafta Alkhazraji, and Abir Jaafar Hussain. "Heart Attack Prediction by Integrating Independent Component Analysis with Machine Learning Classifiers." In 2024 17th International Conference on Development in eSystem Engineering (DeSE). IEEE, 2024. https://doi.org/10.1109/dese63988.2024.10912055.

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Jayabalan, Anandhi, R. Uma, and S. Padmakala. "Comprehensive Heart Attack Prediction Model Using Stacked Ensembles and Clinical Feature Engineering." In 2025 International Conference on Multi-Agent Systems for Collaborative Intelligence (ICMSCI). IEEE, 2025. https://doi.org/10.1109/icmsci62561.2025.10894120.

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Reports on the topic "Heart Attack prediction"

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Mullainathan, Sendhil, and Ziad Obermeyer. A Machine Learning Approach to Low-Value Health Care: Wasted Tests, Missed Heart Attacks and Mis-Predictions. National Bureau of Economic Research, 2019. http://dx.doi.org/10.3386/w26168.

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Floyd, Jason, and Daniel Madrzykowski. Analysis of a Near Miss in a Garden Apartment Fire – Georgia 2022. UL's Fire Safety Research Institute, 2022. http://dx.doi.org/10.54206/102376/rsfd6862.

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On February 9, 2022, Cobb County Fire and Emergency Services responded to a fire in a ground floor unit in a garden apartment building. At arrival, the fire was a post-flashover fire in a bedroom. Initial fire control was attempted by an interior fire attack team which was unable to quickly locate the fire. Exterior suppression through the bedroom window was started prior to discovery of the fire by the interior team. Shortly after fire discovery by the internal team, a mayday was called. Four firefighters from the interior fire attack team received first and second degree burns. This report a
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Lukezich. L51775 Susceptibility of Modern ERW Pipe to Selective Weld Seam Corrosion in Wet Environments. Pipeline Research Council International, Inc. (PRCI), 1998. http://dx.doi.org/10.55274/r0010424.

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Grooving corrosion is a phenomenon by which the weld seam of electric resistance welded (ERW) pipe is preferentially attacked in wet natural gas environments containing CO,. The attack initiates as an aligned string of pits which grow and intersect, forming a round-bottomed groove of damage centered on the weld seam. The susceptibility of ERW pipe to this damage mechanism is known to be related to the chemical composition (particularly the sulfur content) of the pipe, the welding process employed, and the use of a post weld heat treatment. Of particular concern to the natural gas pipeline indu
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