{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T03:14:07Z","timestamp":1743131647541,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":31,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789811958670"},{"type":"electronic","value":"9789811958687"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-981-19-5868-7_35","type":"book-chapter","created":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T07:49:15Z","timestamp":1672559355000},"page":"481-492","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Unexpected Alliance of Cardiovascular Diseases and Artificial Intelligence in Health Care"],"prefix":"10.1007","author":[{"given":"Rishika","family":"Anand","sequence":"first","affiliation":[]},{"given":"S. R. N.","family":"Reddy","sequence":"additional","affiliation":[]},{"given":"Dinesh","family":"Kumar","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,1,1]]},"reference":[{"key":"35_CR1","unstructured":"WHO (2020) Cardiovascular Diseases (CVDs). In: WHO. https:\/\/www.who.int\/health-topics\/cardiovascular-diseases#tab=tab_1. Accessed 14 Sep 2020"},{"key":"35_CR2","doi-asserted-by":"publisher","DOI":"10.1155\/2017\/7483639","author":"T Shu","year":"2017","unstructured":"Shu T, Zhang B, Tang YY (2017) Effective heart disease detection based on quantitative computerized traditional Chinese medicine using representation based classifiers. Evid-Based Complement Altern Med. https:\/\/doi.org\/10.1155\/2017\/7483639","journal-title":"Evid-Based Complement Altern Med"},{"key":"35_CR3","doi-asserted-by":"publisher","DOI":"10.3390\/app8122344","author":"SGY Yaseen","year":"2018","unstructured":"Yaseen SGY, Kwon S (2018) Classification of heart sound signal using multiple features. Appl Sci (Switzerland). https:\/\/doi.org\/10.3390\/app8122344","journal-title":"Appl Sci (Switzerland)"},{"key":"35_CR4","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1016\/j.hlc.2019.05.170","volume":"29","author":"A Harky","year":"2020","unstructured":"Harky A, Chaplin G, Chan JSK et al (2020) The future of open heart surgery in the era of robotic and minimal surgical interventions. Heart Lung Circ 29:49\u201361","journal-title":"Heart Lung Circ"},{"key":"35_CR5","doi-asserted-by":"publisher","first-page":"2668","DOI":"10.1016\/j.jacc.2018.03.521","volume":"71","author":"KW Johnson","year":"2018","unstructured":"Johnson KW, Torres Soto J, Glicksberg BS et al (2018) Artificial intelligence in cardiology. J Am Coll Cardiol 71:2668\u20132679","journal-title":"J Am Coll Cardiol"},{"key":"35_CR6","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1016\/j.bpobgyn.2017.09.005","volume":"46","author":"C Lonnerfors","year":"2018","unstructured":"Lonnerfors C (2018) Robot-assisted myomectomy. Best Pract Res Clin Obstet Gynaecol 46:113\u2013119","journal-title":"Best Pract Res Clin Obstet Gynaecol"},{"key":"35_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.eururo.2018.11.037","author":"GE Cacciamani","year":"2018","unstructured":"Cacciamani GE, de Marco V, Sebben M, Rizzetto R (2018) Robot-assisted Vescica Ileale Padovana: a new technique for intracorporeal bladder replacement reproducing open surgical principles. Eur Urol. https:\/\/doi.org\/10.1016\/j.eururo.2018.11.037","journal-title":"Eur Urol"},{"key":"35_CR8","first-page":"233","volume":"8","author":"M Sharma","year":"2019","unstructured":"Sharma M (2019) ECG and medical diagnosis based recognition & prediction of cardiac disease using deep learning. Int J Sci Technol Res 8:233\u2013240","journal-title":"Int J Sci Technol Res"},{"key":"35_CR9","doi-asserted-by":"crossref","unstructured":"Ankireddy S (2020) A novel approach to the diagnosis of heart disease using machine learning and deep neural networks","DOI":"10.1109\/URTC49097.2019.9660581"},{"key":"35_CR10","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-019-0244-x","author":"FI Alarsan","year":"2019","unstructured":"Alarsan FI, Younes M (2019) Analysis and classification of heart diseases using heartbeat features and machine learning algorithms. J Big Data. https:\/\/doi.org\/10.1186\/s40537-019-0244-x","journal-title":"J Big Data"},{"key":"35_CR11","doi-asserted-by":"publisher","unstructured":"Fredrick David BH, Benjamin Fredrick David H, Antony Belcy S (2018) Heart disease prediction using data mining techniques. J Soft Comput 1824\u20131831. https:\/\/doi.org\/10.21917\/ijsc.2018.0254","DOI":"10.21917\/ijsc.2018.0254"},{"key":"35_CR12","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1038\/s41551-018-0195-0","volume":"2","author":"R Poplin","year":"2018","unstructured":"Poplin R, Varadarajan A, Blumer K et al (2018) Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nat Biomed Eng 2:158\u2013164. https:\/\/doi.org\/10.1038\/s41551-018-0195-0","journal-title":"Nat Biomed Eng"},{"key":"35_CR13","unstructured":"Hossain E, Al-Mamun A (2019) Early heart attack prediction using machine learning technique"},{"key":"35_CR14","doi-asserted-by":"crossref","unstructured":"Kotanidis CP, Antoniades C (2020) Selfies in cardiovascular medicine: welcome to a new era of medical diagnostics. Eur Heart J 41:4412\u20134414","DOI":"10.1093\/eurheartj\/ehaa608"},{"key":"35_CR15","doi-asserted-by":"crossref","unstructured":"Singh S, Penzel T, Engineering E, Delhi N (2020) Irregularities using machine learning 438\u2013442","DOI":"10.1109\/ICPC2T48082.2020.9071495"},{"key":"35_CR16","first-page":"1126","volume":"13","author":"PK Vemuri","year":"2019","unstructured":"Vemuri PK, Kunta A, Challagulla R et al (2019) Artificial intelligence and internet of medical things based health-care system for real-time maternal stress\u2014strategies to reduce maternal mortality rate. Drug Invent Today 13:1126\u20131129","journal-title":"Drug Invent Today"},{"key":"35_CR17","doi-asserted-by":"publisher","first-page":"81542","DOI":"10.1109\/ACCESS.2019.2923707","volume":"7","author":"S Mohan","year":"2019","unstructured":"Mohan S, Thirumalai C, Srivastava G (2019) Effective heart disease prediction using hybrid machine learning techniques. IEEE Access 7:81542\u201381554. https:\/\/doi.org\/10.1109\/ACCESS.2019.2923707","journal-title":"IEEE Access"},{"key":"35_CR18","doi-asserted-by":"publisher","unstructured":"Bakar WAWA, Man M, Awang WSW et al (2020) HDP: heart disease prediction tool using neural network. Int J Emerg Trends Eng Res 8:1794\u20131797. https:\/\/doi.org\/10.30534\/ijeter\/2020\/50852020","DOI":"10.30534\/ijeter\/2020\/50852020"},{"key":"35_CR19","doi-asserted-by":"crossref","unstructured":"Maheswari KU (2017) Neural network based heart disease prediction. IJERT 5:1\u20134","DOI":"10.1155\/2017\/2780501"},{"key":"35_CR20","doi-asserted-by":"crossref","unstructured":"Moradi M, Madani A, Karargyris A, Syeda-Mahmood TF (2018) Chest x-ray generation and data augmentation for cardiovascular abnormality classification. SPIE-Int Soc Opt Eng 57","DOI":"10.1117\/12.2293971"},{"key":"35_CR21","doi-asserted-by":"publisher","DOI":"10.1093\/eurheartj\/ehaa640","author":"S Lin","year":"2020","unstructured":"Lin S, Li Z, Fu B et al (2020) Feasibility of using deep learning to detect coronary artery disease based on facial photo. Eur Heart J. https:\/\/doi.org\/10.1093\/eurheartj\/ehaa640","journal-title":"Eur Heart J"},{"key":"35_CR22","doi-asserted-by":"crossref","unstructured":"Chokwijitkul T, Nguyen A, Hassanzadeh H, Perez S (2018) Identifying risk factors for heart disease in electronic medical records: a deep learning approach","DOI":"10.18653\/v1\/W18-2303"},{"key":"35_CR23","unstructured":"Mahmoud M, Amen K, Zohdy M, Machine learning for multiple stage heart disease prediction"},{"key":"35_CR24","unstructured":"Yang X, Gong Y, Waheed N et al Identifying cancer patients at risk for heart failure using machine learning methods"},{"key":"35_CR25","doi-asserted-by":"publisher","unstructured":"Ambekar S, Phalnikar R (2018) Disease risk prediction by using convolutional neural network. In: Proceedings\u20142018 4th international conference on computing, communication control and automation, ICCUBEA 2018. https:\/\/doi.org\/10.1109\/ICCUBEA.2018.8697423","DOI":"10.1109\/ICCUBEA.2018.8697423"},{"key":"35_CR26","doi-asserted-by":"publisher","unstructured":"Islam Chowdhuryy MH, Sultana M, Ghosh R et al (2018) AI assisted portable ECG for fast and patient specific diagnosis. In: International conference on computer, communication, chemical, material and electronic engineering, IC4ME2 2018, pp 4\u20137. https:\/\/doi.org\/10.1109\/IC4ME2.2018.8465483","DOI":"10.1109\/IC4ME2.2018.8465483"},{"key":"35_CR27","doi-asserted-by":"publisher","unstructured":"Amiriparian S, Schmitt M, Cummins N et al (2018) Deep unsupervised representation learning for abnormal heart sound classification. In: Proceedings of the annual international conference of the IEEE engineering in medicine and biology society, EMBS, pp 4776\u20134779. https:\/\/doi.org\/10.1109\/EMBC.2018.8513102","DOI":"10.1109\/EMBC.2018.8513102"},{"key":"35_CR28","doi-asserted-by":"publisher","unstructured":"Rajliwall NS, Davey R, Chetty G (2019) Machine learning based models for cardiovascular risk prediction. In: Proceedings\u2014international conference on machine learning and dataengineering, iCMLDE, pp 149\u2013153. https:\/\/doi.org\/10.1109\/iCMLDE.2018.00034","DOI":"10.1109\/iCMLDE.2018.00034"},{"key":"35_CR29","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1088\/1757-899X\/1022\/1\/012046","volume":"1022","author":"A Garg","year":"2021","unstructured":"Garg A, Sharma B, Khan R (2021) Heart disease prediction using machine learning techniques. IOP Conf Ser Mater Sci Eng 1022:93\u201396. https:\/\/doi.org\/10.1088\/1757-899X\/1022\/1\/012046","journal-title":"IOP Conf Ser Mater Sci Eng"},{"key":"35_CR30","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1097\/HCO.0000000000000694","volume":"35","author":"LD Sacks","year":"2020","unstructured":"Sacks LD, Axelrod DM (2020) Virtual reality in pediatric cardiology: hype or hope for the future? Curr Opin Cardiol 35:37\u201341","journal-title":"Curr Opin Cardiol"},{"key":"35_CR31","doi-asserted-by":"publisher","first-page":"144","DOI":"10.1016\/j.tcm.2017.08.003","volume":"28","author":"JM Pevnick","year":"2018","unstructured":"Pevnick JM, Birkeland K, Zimmer R et al (2018) Wearable technology for cardiology: an update and framework for the future. Trends Cardiovasc Med 28:144\u2013150","journal-title":"Trends Cardiovasc Med"}],"container-title":["Lecture Notes in Electrical Engineering","Machine Learning, Image Processing, Network Security and Data Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-19-5868-7_35","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,11]],"date-time":"2024-10-11T11:10:40Z","timestamp":1728645040000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-19-5868-7_35"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9789811958670","9789811958687"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-981-19-5868-7_35","relation":{},"ISSN":["1876-1100","1876-1119"],"issn-type":[{"type":"print","value":"1876-1100"},{"type":"electronic","value":"1876-1119"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"1 January 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}