Literatura académica sobre el tema "HEART DISEASE PREDICTION"
Crea una cita precisa en los estilos APA, MLA, Chicago, Harvard y otros
Consulte las listas temáticas de artículos, libros, tesis, actas de conferencias y otras fuentes académicas sobre el tema "HEART DISEASE PREDICTION".
Junto a cada fuente en la lista de referencias hay un botón "Agregar a la bibliografía". Pulsa este botón, y generaremos automáticamente la referencia bibliográfica para la obra elegida en el estilo de cita que necesites: APA, MLA, Harvard, Vancouver, Chicago, etc.
También puede descargar el texto completo de la publicación académica en formato pdf y leer en línea su resumen siempre que esté disponible en los metadatos.
Artículos de revistas sobre el tema "HEART DISEASE PREDICTION"
Muthulakshmi, P., M. Parveen y P. Rajeswari. "Prediction of Heart Disease using Ensemble Learning". Indian Journal Of Science And Technology 16, n.º 20 (27 de mayo de 2023): 1469–76. http://dx.doi.org/10.17485/ijst/v16i20.2279.
Texto completoLu, Yi. "Heart Disease Prediction Model based on Prophet". Highlights in Science, Engineering and Technology 39 (1 de abril de 2023): 1035–40. http://dx.doi.org/10.54097/hset.v39i.6700.
Texto completoRani, K. Sandhya, M. Sai Manoj y G. Suguna Mani. "A Heart Disease Prediction Model using Logistic Regression". International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (30 de abril de 2018): 1463–66. http://dx.doi.org/10.31142/ijtsrd11401.
Texto completoVinothini, S., Ishaan Singh, Sujaya Pradhan y Vipul Sharma. "Heart Disease Prediction". International Journal of Engineering & Technology 7, n.º 3.12 (20 de julio de 2018): 750. http://dx.doi.org/10.14419/ijet.v7i3.12.16494.
Texto completoKumar, Prof K. Senthil. "HEART DISEASE PREDICTION USING MACHINE LEARNING". INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, n.º 12 (1 de diciembre de 2023): 1–11. http://dx.doi.org/10.55041/ijsrem27570.
Texto completoBavani, B., S. Nirmala Sugirtha Rajini, M. S. Josephine y V. Prasannakumari. "Heart Disease Prediction System based on Decision Tree Classifier". Journal of Advanced Research in Dynamical and Control Systems 11, n.º 10-SPECIAL ISSUE (31 de octubre de 2019): 1232–37. http://dx.doi.org/10.5373/jardcs/v11sp10/20192968.
Texto completoAhmad, Bamanga Mahmud, Ahmadu Asabe Sandra, Musa Yusuf Malgwi y Dahiru I. Sajoh. "Ensemble model for Heart Disease Prediction". Science Progress and Research 1, n.º 4 (5 de octubre de 2021): 268–80. http://dx.doi.org/10.52152/spr/2021.145.
Texto completoKhan, Sundas Naqeeb, Nazri Mohd Nawi, Asim Shahzad, Arif Ullah, Muhammad Faheem Mushtaq, Jamaluddin Mir y Muhammad Aamir. "Comparative Analysis for Heart Disease Prediction". JOIV : International Journal on Informatics Visualization 1, n.º 4-2 (15 de noviembre de 2017): 227. http://dx.doi.org/10.30630/joiv.1.4-2.66.
Texto completoPati, Abhilash, Manoranjan Parhi y Binod Kumar Pattanayak. "IHDPM: an integrated heart disease prediction model for heart disease prediction". International Journal of Medical Engineering and Informatics 14, n.º 6 (2022): 1. http://dx.doi.org/10.1504/ijmei.2022.10044903.
Texto completoPati, Abhilash, Manoranjan Parhi y Binod Kumar Pattanayak. "IHDPM: an integrated heart disease prediction model for heart disease prediction". International Journal of Medical Engineering and Informatics 14, n.º 6 (2022): 564. http://dx.doi.org/10.1504/ijmei.2022.126526.
Texto completoTesis sobre el tema "HEART DISEASE PREDICTION"
Bolton, Jennifer Lynn. "Candidate genotypes in prediction of coronary heart disease". Thesis, University of Edinburgh, 2011. http://hdl.handle.net/1842/15877.
Texto completoNet, J. B. van der. "Towards genetic prediction of coronary heart disease in familial hypercholesterolemia". [S.l.] : Rotterdam : [The Author] ; Erasmus University [Host], 2009. http://hdl.handle.net/1765/14566.
Texto completoVan, Zyl Johet Engela. "Accuracy of risk prediction tools for acute coronary syndrome : a systematic review". Thesis, Stellenbosch : Stellenbosch University, 2015. http://hdl.handle.net/10019.1/97069.
Texto completoENGLISH ABSTRACT: Background: Coronary artery disease is a form of cardiovascular disease (CVD) which manifests itself in three ways: angina pectoris, acute coronary syndrome and cardiac death. Thirty-three people die daily of a myocardial infarction (cardiac death) and 7.5 million deaths annually are caused by CVD (51% from strokes and 45% from coronary artery disease) worldwide. Globally, the CVD death rate is a mere 4% compared to South Africa which has a 42% death rate. It is predicted that by the year 2030 there will be 25 million deaths annually from CVD, mainly in the form of strokes and heart disease. The WHO compared the death rates of high-income countries to those of low- and middle-income countries, like South Africa, and the results show that CVD deaths are declining in high-income countries but rapidly increasing in low- and middle-income countries. Although there are several risk prediction tools in use worldwide, to predict ischemic risk, South Africa does not use any of these tools. Current practice in South Africa to diagnose acute coronary syndrome is the use of a physical examination, ECG changes and positive serum cardiac maker levels. Internationally the same practice is used to diagnose acute coronary syndrome but risk assessment tools are used additionally to this practise because of limitations of the ECG and serum cardiac markers when it comes to NSTE-ACS. Objective: The aim of this study was to systematically appraise evidence on the accuracy of acute coronary syndrome risk prediction tools in adults. Methods: An extensive literature search of studies published in English was undertaken. Electronic databases searched were Cochrane Library, MEDLINE, Embase and CINAHL. Other sources were also searched, and cross-sectional studies, cohort studies and randomised controlled trials were reviewed. All articles were screened for methodological quality by two reviewers independently with the QUADAS-2 tool which is a standardised instrument. Data was extracted using an adapted Cochrane data extraction tool. Data was entered in Review Manager 5.2 software for analysis. Sensitivity and specificity was calculated for each risk score and an SROC curve was created. This curve was used to evaluate and compare the prediction accuracy of each test. Results: A total of five studies met the inclusion criteria of this review. Two HEART studies and three GRACE studies were included. In all, 9 092 patients participated in the selected studies. Estimates of sensitivity for the HEART risks score (two studies, 3268 participants) were 0,51 (95% CI 0,46 to 0,56) and 0,68 (95% CI 0,60 to 0,75); specificity for the HEART risks score was 0,90 (95% CI 0,88 to 0,91) and 0,92 (95% CI 0,90 to 0,94). Estimates of sensitivity for the GRACE risk score (three studies, 5824 participants) were 0,03 (95% CI0,01 to 0,05); 0,20 (95% CI 0,14 to 0,29) and 0,79 (95% CI 0,58 to 0,93). The specificity was 1,00 (95% CI 0,99 to 1,00); 0,97 (95% CI 0,95 to 0,98) and 0,78 (95% CI 0,73 to 0,82). On the SROC curve analysis, there was a trend for the GRACE risk score to perform better than the HEART risk score in predicting acute coronary syndrome in adults. Conclusion: Both risk scores showed that they had value in accurately predicting the presence of acute coronary syndrome in adults. The GRACE showed a positive trend towards better prediction ability than the HEART risk score.
AFRIKAANSE OPSOMMING: Agtergrond: Koronêre bloedvatsiekte is ‘n vorm van kardiovaskulêre siekte. Koronêre hartsiekte manifesteer in drie maniere: angina pectoris, akute koronêre sindroom en hartdood. Drie-en-dertig mense sterf daagliks aan ‘n miokardiale infarksie (hartdood). Daar is 7,5 miljoen sterftes jaarliks as gevolg van kardiovaskulêre siektes (51% deur beroertes en 45% as gevolg van koronêre hartsiektes) wêreldwyd. Globaal is die sterfte syfer as gevolg van koronêre vaskulêre siekte net 4% in vergelyking met Suid Afrika, wat ‘n 42% sterfte syfer het. Dit word voorspel dat teen die jaar 2030 daar 25 miljoen sterfgevalle jaarliks sal wees, meestal toegeskryf aan kardiovaskulêre siektes. Die hoof oorsaak van sterfgevalle sal toegeskryf word aan beroertes en hart siektes. Die WHO het die sterf gevalle van hoeinkoms lande vergelyk met die van lae- en middel-inkoms lande, soos Suid Afrika, en die resultate het bewys dat sterf gevalle as gevolg van kardiovaskulêre siekte is besig om te daal in hoe-inkoms lande maar dit is besig om skerp te styg in lae- en middel-inkoms lande. Daar is verskeie risiko-voorspelling instrumente wat wêreldwyd gebruik word om isgemiese risiko te voorspel, maar Suid Afrika gebruik geen van die risiko-voorspelling instrumente nie. Huidiglik word akute koronêre sindroom gediagnoseer met die gebruik van n fisiese ondersoek, EKG verandering en positiewe serum kardiale merkers. Internationaal word die selfde gebruik maar risiko-voorspelling instrumente word aditioneel by gebruik omdat daar limitasies is met EKG en serum kardiale merkers as dit by NSTE-ACS kom. Doelwit: Die doel van hierdie sisematiese literatuuroorsig was om stelselmatig die bewyse te evalueer oor die akkuraatheid van akute koronêre sindroom risiko-voorspelling instrumente vir volwassenes. Metodes: 'n Uitgebreide literatuursoektog van studies wat in Engels gepubliseer is was onderneem. Cochrane biblioteek, MEDLINE, Embase en CINAHL databases was deursoek. Ander bronne is ook deursoek. Die tiepe studies ingesluit was deurnsee-studies, kohortstudies en verewekansigde gekontroleerde studies. Alle artikels is onafhanklik vir die metodologiese kwaliteit gekeur deur twee beoordeelaars met die gebruik van die QUADAS-2 instrument, ‘n gestandaardiseerde instrument. ‘n Aangepaste Cochrane data instrument is gebruik om data te onttrek. Data is opgeneem in Review Manager 5.2 sagteware vir ontleding. Sensitiwiteit en spesifisiteit is bereken vir elke risiko instrument en ‘n SROC kurwe is geskep. Die SROC kurwe is gebruik om die akkuraatheid van voorspelling van elke instrument te evalueer en te toets. Resultate: Twee HEART studies en drie GRACE studies is ingesluit. In total was daar 9 092 patiente wat deelgeneeem het in die gekose studies. Skattings van sensitiwiteit vir die HEART risiko instrument (twee studies, 3268 deelnemers) was 0,51 (95% CI 0,47 to 0,56) en 0,68 (95% CI 0,60 to 0,75) spesifisiteit vir die HEART risiko instrument was 0,89 (95% CI 0,88 to 0,91) en 0,92 (95% CI 0,90 to 0,94). Skattings van sensitiwiteit vir die GRACE risiko instrument (drie studies, 5824 deelnemers) was 0,28 (95% CI 0,13 to 0,53); 0,20 (95% CI 0,14 to 0,29) en 0,79 (95% CI 0,58 to 0,93). Die spesifisiteit vir die GRACE risiko instrument was 0,97 (95% CI 0,95 to 0,99); 0,97 (95% CI 0,95 to 0,98) en 0,78 (95% CI 0,73 to 0,82). Met die SROC kurwe ontleding was daar ‘n tendens vir die GRACE risiko instrument om beter te vaar as die HEART risiko instrument in die voorspelling van akute koronêre sindroom in volwassenes. Gevolgtrekking: Altwee risiko instrumente toon aan dat albei instrumente van waarde is. Albei het die vermoë om die teenwoordigheid van akute koronêre sindroom in volwassenes te voorspel. Die GRACE toon ‘n positiewe tendens teenoor beter voorspelling vermoë as die HEART risiko instrument.
Savatteri, Giuseppe. "Enviromental factors influencing heart diseases". Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020.
Buscar texto completoLi, Jianeng. "Research on a Heart Disease Prediction Model Based on the Stacking Principle". Thesis, Högskolan Dalarna, Informatik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:du-34591.
Texto completoRice, Thomas H. "Prediction of oxygen consumption during exercise testing in apparently healthy subjects and coronary artery disease patients". Thesis, Virginia Polytechnic Institute and State University, 1986. http://hdl.handle.net/10919/91143.
Texto completoM.S.
Vedin, Ola. "Prevalence and Prognostic Impact of Periodontal Disease and Conventional Risk Factors in Patients with Stable Coronary Heart Disease". Doctoral thesis, Uppsala universitet, Institutionen för medicinska vetenskaper, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-260564.
Texto completoAtsalakis, Mihalis. "Prediction of initial involvement of first grade Greek school children in an out-of-school, organized, community physical activity programme : an application of the theory of planned behaviour". Thesis, University of Hull, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.262408.
Texto completoZuo, Heng. "3D Multi-Physics MRI-Based Human Right Ventricle Models for Patients with repaired Tetralogy of Fallot: Cardiac Mechanical Analysis and Surgical Outcome Prediction". Digital WPI, 2017. https://digitalcommons.wpi.edu/etd-dissertations/478.
Texto completoЗадойоний, Віктор Андрійович y Viktor Zadoionyi. "Комп’ютерна система прогнозування ризиків появи серцевих захворювань". Bachelor's thesis, Тернопільський національний технічний університет імені Івана Пулюя, 2021. http://elartu.tntu.edu.ua/handle/lib/35577.
Texto completoThe computer system for predicting the risk of heart disease has been designed in the bachelor's thesis. It consists of two main components: a subsystem for collecting data on human vital signs and an intelligent module for predicting the risk of heart disease. The human vital signs collection subsystem is implemented using mobile sensors attached to the human body, a single-chip Raspberry PI mini-computer, as a central node that acts as a controller for data transmission and cloud storage - to store and analyze the collected information. The following sensors attached to the human body: a heart rate monitor, a body temperature sensor and a blood pressure monitor. In addition, sensors for analyzing air parameters: temperature, humidity, the presence of harmful substances and light intensity were using to take into account the parameters of the environment in which a person is. The intelligent module for predicting the risk of heart disease is implemented using the Python programming language and the use of open machine learning libraries. The simulation used and investigated the characteristics of six models of binary classification, as a result of which it was found that the most effective model is based on random forests (Random Forest), which provides forecasting accuracy of 97.5%.
ПЕРЕЛІК ОСНОВНИХ УМОВНИХ ПОЗНАЧЕНЬ, СИМВОЛІВ І СКОРОЧЕНЬ 7 ВСТУП 8 РОЗДІЛ 1 АНАЛІЗ ВИМОГ ТА ОСОБЛИВОСТЕЙ ПРОЕКТУВАННЯ КОМП’ЮТЕРНИХ СИСТЕМ МЕДИЧНОГО ПРИЗНАЧЕННЯ 9 1.1 Аналіз технічного завдання на проектування комп’ютерної системи прогнозування ризиків появи серцевих захворювань 9 1.2 Обґрунтування доцільності та особливості проектування комп’ютерної системи 17 РОЗДІЛ 2 МОДЕЛЬ ТА СТРУКТУРА КОМП’ЮТЕРНОЇ СИСТЕМИ ПРОГНОЗУВАННЯ РИЗИКІВ ПОЯВИ СЕРЦЕВИХ ЗАХВОРЮВАНЬ 22 2.1 Моделі та архітектури комп’ютерних систем у сфері охорони здоров’я 22 2.2 Фактори впливу навколишнього середовища на стан організму людини та комп’ютерна система автоматизованого їх аналізу 25 2.3 Різновиди та розвиток IoT у медичній галузі 29 2.4 Проектування архітектури комп’ютерної системи прогнозування ризиків появи серцевих захворювань 34 РОЗДІЛ 3 ПРОГРАМНА МОДЕЛЬ ІНТЕЛЕКТУАЛЬНОГО МОДУЛЯ ПРОГНОЗУВАННЯ РИЗИКІВ ВИНИКНЕННЯ СЕРЦЕВИХ ЗАХВОРЮВАНЬ 42 3.1 Аналіз відкритих джерел даних для побудови моделі прогнозування щодо виникнення серцевих захворювань 42 3.2 Препроцесинг даних 44 3.3 Виявлення залежностей між ознаками вхідного набору даних 51 3.4 Виявлення значимих ознак набору даних 56 3.5 Реалізація моделей прогнозування розвитку серцевих захворювань 59 РОЗДІЛ 4 БЕЗПЕКА ЖИТТЄДІЯЛЬНОСТІ, ОСНОВИ ОХОРОНИ ПРАЦІ 67 ВИСНОВКИ 68 СПИСОК ВИКОРИСТАНИХ ДЖЕРЕЛ 69 Додаток A. Технічне завдання
Libros sobre el tema "HEART DISEASE PREDICTION"
A, De Lemos James y American Heart Association, eds. Biomarkers in heart disease. Malden, Mass: Blackwell, 2008.
Buscar texto completoA, De Lemos James y American Heart Association, eds. Biomarkers in heart disease. Malden, Mass: Blackwell Pub., 2008.
Buscar texto completoAccuracy of a treadmill scoring system for prediction of coronary artery disease in female subjects. 1991.
Buscar texto completoKornitzer, M. y R. Goldberg. Contribution of Long-term Follow-up to the Prediction of Coronary Heart Disease (Cardiology). S Karger Ag, 1993.
Buscar texto completoMills, Gary H. Pulmonary disease and anaesthesia. Editado por Philip M. Hopkins. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199642045.003.0082.
Texto completoMasuda, Atsuro, Masanao Naya, Keiichiro Yoshinaga y Nagara Tamaki. Imaging of Myocardial Innervation. Oxford University Press, 2015. http://dx.doi.org/10.1093/med/9780199392094.003.0023.
Texto completoCardiac patients' attitude toward adherence to an exercise medical regimen. 1988.
Buscar texto completoCardiac patients' attitude toward adherence to an exercise medical regimen. 1988.
Buscar texto completoCardiac patients' attitude toward adherence to an exercise medical regimen. 1985.
Buscar texto completoCardiac patients' attitude toward adherence to an exercise medical regimen. 1988.
Buscar texto completoCapítulos de libros sobre el tema "HEART DISEASE PREDICTION"
Dutta, Pijush, Shobhandeb Paul, Neha Shaw, Susmita Sen y Madhurima Majumder. "Heart Disease Prediction". En Artificial Intelligence and Cybersecurity, 1–18. Boca Raton: CRC Press, 2021. http://dx.doi.org/10.1201/9781003097518-1.
Texto completoNeto, Cristiana, Diana Ferreira, José Ramos, Sandro Cruz, Joaquim Oliveira, António Abelha y José Machado. "Prediction Models for Coronary Heart Disease". En Distributed Computing and Artificial Intelligence, Volume 1: 18th International Conference, 119–28. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86261-9_12.
Texto completoJha, Rahul Kumar, Santosh Kumar Henge y Ashok Sharma. "Heart Disease Prediction and Hybrid GANN". En Intelligent and Fuzzy Techniques for Emerging Conditions and Digital Transformation, 438–45. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-85577-2_52.
Texto completoAdhikari, Bikal y Subarna Shakya. "Heart Disease Prediction Using Ensemble Model". En Lecture Notes in Networks and Systems, 857–68. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-7657-4_69.
Texto completoSai Shekhar, M., Y. Mani Chand y L. Mary Gladence. "Heart Disease Prediction Using Machine Learning". En Advances in Systems, Control and Automations, 603–9. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8685-9_63.
Texto completoPatel, Jaydutt, Azhar Ali Khaked, Jitali Patel y Jigna Patel. "Heart Disease Prediction Using Machine Learning". En Proceedings of Second International Conference on Computing, Communications, and Cyber-Security, 653–65. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0733-2_46.
Texto completoSharma, Sumit, Vishu Madaan, Prateek Agrawal y Narendra Kumar Garg. "Heart Disease Prediction Using Fuzzy System". En Communications in Computer and Information Science, 424–34. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-3140-4_38.
Texto completoBhatia, Meenu y Dilip Motwani. "Heart Disease Prediction Using Ensemblers Learning". En Information and Communication Technology for Intelligent Systems, 733–43. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-7078-0_73.
Texto completoPatil, Saraswati, Pavan Kumar Sanjay, Harsh Pardeshi, Niraj Patil, Omkar Pawar y Prishita Jhamtani. "Heart Disease Prediction Using Supervised Learning". En ICT for Intelligent Systems, 385–94. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3982-4_33.
Texto completoSiva Rama Krishna, Ch, M. Vasanthi, K. Hemanth Reddy y G. Jaswanth. "Heart Disease Prediction Using Machine Learning". En Intelligent Manufacturing and Energy Sustainability, 589–95. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-8497-6_53.
Texto completoActas de conferencias sobre el tema "HEART DISEASE PREDICTION"
Javangula, Upagnaa, Teja Rani Banna y Janardhana Rao Alapati. "Heart Disease Prediction". En 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2023. http://dx.doi.org/10.1109/icccnt56998.2023.10306980.
Texto completoVeena, N., S. Mahalakshmi, G. Anisha Diyya, Alekhya Allada y Malavika S. Anand. "Heart Disease Prediction System". En 2021 International Conference on Forensics, Analytics, Big Data, Security (FABS). IEEE, 2021. http://dx.doi.org/10.1109/fabs52071.2021.9702552.
Texto completoMahfuri, Mahmoud, Taher M. Ghazal, Muhammad Mudassar, Shahan Yamin Siddiqui, Sajid Farooq, Nayab Kanwal y Munir Ahmad. "Medical Diagnoses: Heart Disease Prediction". En 2023 International Conference on Business Analytics for Technology and Security (ICBATS). IEEE, 2023. http://dx.doi.org/10.1109/icbats57792.2023.10111497.
Texto completoDoki, Srichand, Siddhartha Devella, Sumanth Tallam, Sai Sujeeth Reddy Gangannagari, P. Sampathkrishna Reddy y G. Pradeep Reddy. "Heart Disease Prediction Using XGBoost". En 2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT). IEEE, 2022. http://dx.doi.org/10.1109/icicict54557.2022.9917678.
Texto completoRahman, Mafizur, Maryam Mehzabin Zahin y Linta Islam. "Effective Prediction On Heart Disease: Anticipating Heart Disease Using Data Mining Techniques". En 2019 International Conference on Smart Systems and Inventive Technology (ICSSIT). IEEE, 2019. http://dx.doi.org/10.1109/icssit46314.2019.8987776.
Texto completoBilgaiyan, Saurabh, Tajul Islam Ayon, Aliza Ahmed Khan, Fatema Tuj Johora, Masuma Parvin y Mohammad Jahangir Alam. "Heart disease Prediction Using Machine Learning". En 2023 International Conference on Computer Communication and Informatics (ICCCI). IEEE, 2023. http://dx.doi.org/10.1109/iccci56745.2023.10128378.
Texto completoGhazal, Taher M., Amer Ibrahim, Ali Sheraz Akram, Zahid Hussain Qaisar, Sundus Munir y Shanza Islam. "Heart Disease Prediction Using Machine Learning". En 2023 International Conference on Business Analytics for Technology and Security (ICBATS). IEEE, 2023. http://dx.doi.org/10.1109/icbats57792.2023.10111368.
Texto completoSong, Siyue, Tianhua Chen y Grigoris Antoniou. "ANFIS Models for Heart Disease Prediction". En ICIAI 2021: 2021 the 5th International Conference on Innovation in Artificial Intelligence. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3461353.3461354.
Texto completoSanyal, Saptarsi, Dolly Das, Saroj Kumar Biswas, Manomita Chakraborty y Biswajit Purkayastha. "Heart Disease Prediction Using Classification Models". En 2022 3rd International Conference for Emerging Technology (INCET). IEEE, 2022. http://dx.doi.org/10.1109/incet54531.2022.9824651.
Texto completoSrivastava, Asmit y Ashish kumar Singh. "Heart Disease Prediction using Machine Learning". En 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE). IEEE, 2022. http://dx.doi.org/10.1109/icacite53722.2022.9823584.
Texto completoInformes sobre el tema "HEART DISEASE PREDICTION"
Kent, David M., Jason Nelson, Jenica N. Upshaw, Gaurav Gulati, Riley Brazil, Esmee Venema, Christine M. Lundquist et al. Using Different Data Sets to Test How Well Clinical Prediction Models Work to Predict Patients' Risk of Heart Disease. Patient-Centered Outcomes Research Institute (PCORI), septiembre de 2021. http://dx.doi.org/10.25302/09.2021.me.160635555.
Texto completoWei, Dongmei, Yang Sun y Rongtao Chen. Risk prediction model for ISR after coronary stenting-a systematic review and meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, abril de 2023. http://dx.doi.org/10.37766/inplasy2023.4.0014.
Texto completoRahman, Kazi, Grace Lee, Kristina Vine, Amba-Rose Atkinson, Michael Tong y Veronica Matthews. Impacts of climate change on health and health services in northern New South Wales: an Evidence Check rapid review. The Sax Institute, diciembre de 2022. http://dx.doi.org/10.57022/xlsj7564.
Texto completo