{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,11]],"date-time":"2026-01-11T03:32:07Z","timestamp":1768102327651,"version":"3.49.0"},"reference-count":34,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,11,12]],"date-time":"2022-11-12T00:00:00Z","timestamp":1668211200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,11,12]],"date-time":"2022-11-12T00:00:00Z","timestamp":1668211200000},"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":["J Reliable Intell Environ"],"published-print":{"date-parts":[[2023,3]]},"DOI":"10.1007\/s40860-022-00192-3","type":"journal-article","created":{"date-parts":[[2022,11,12]],"date-time":"2022-11-12T13:29:36Z","timestamp":1668259776000},"page":"57-85","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Back propagation artificial neural network for diagnose of the heart disease"],"prefix":"10.1007","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8426-596X","authenticated-orcid":false,"given":"Jagmohan","family":"Kaur","sequence":"first","affiliation":[]},{"given":"Baljit S.","family":"Khehra","sequence":"additional","affiliation":[]},{"given":"Amarinder","family":"Singh","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,11,12]]},"reference":[{"key":"192_CR1","unstructured":"CDC Technical report (2019). https:\/\/www.cdc.gov\/datastatistics\/index.html"},{"issue":"9","key":"192_CR2","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1161\/CIR.0000000000000757","volume":"141","author":"SS Virani","year":"2020","unstructured":"Virani SS, Alonso A, Benjamin EJ, Bittencourt MS, Callaway CW, Carson AP, Chamberlain AM, Chang AR, Cheng S, Delling FN (2020) Heart disease and stroke statistics 2020 update: a report from the American Heart Association. Circulation 141(9):139\u2013596","journal-title":"Circulation"},{"key":"192_CR3","doi-asserted-by":"publisher","unstructured":"Zhang Z (2018) Artificial neural network. In: Multivariate time series analysis in climate and environmental research, pp 1\u201335. Springer. https:\/\/doi.org\/10.1016\/B0-12-227410-5\/00837-1","DOI":"10.1016\/B0-12-227410-5\/00837-1"},{"issue":"11","key":"192_CR4","doi-asserted-by":"publisher","first-page":"00938","DOI":"10.1016\/j.heliyon.2018.e00938","volume":"4","author":"OI Abiodun","year":"2018","unstructured":"Abiodun OI, Jantan A, Omolara AE, Dada KV, Mohamed NA, Arshad H (2018) State-of-the-art in artificial neural network applications: a survey. Heliyon 4(11):00938. https:\/\/doi.org\/10.1016\/j.heliyon.2018.e00938","journal-title":"Heliyon"},{"issue":"4","key":"192_CR5","doi-asserted-by":"publisher","first-page":"693","DOI":"10.1007\/s12553-021-00555-5","volume":"11","author":"M Mirbabaie","year":"2021","unstructured":"Mirbabaie M, Stieglitz S, Frick NR (2021) Artificial intelligence in disease diagnostics: a critical review and classification on the current state of research guiding future direction. Health Technol 11(4):693\u2013731. https:\/\/doi.org\/10.1007\/s12553-021-00555-5","journal-title":"Health Technol"},{"key":"192_CR6","doi-asserted-by":"publisher","unstructured":"Rudomin P, Arbib MA, Cervantes-P\u00e9rez F, Romo R (2012) Neuroscience: from neural networks to artificial intelligence: proceedings of a US\u2013Mexico Seminar Held in the City of Xalapa in the State of Veracruz on December 9\u201311, 1991 vol. 4. Springer. https:\/\/doi.org\/10.1007\/978-1-4612-2834-9","DOI":"10.1007\/978-1-4612-2834-9"},{"key":"192_CR7","doi-asserted-by":"publisher","first-page":"772","DOI":"10.1016\/j.rser.2013.08.055","volume":"33","author":"AK Yadav","year":"2014","unstructured":"Yadav AK, Chandel SS (2014) Solar radiation prediction using artificial neural network techniques: a review. Renew Sustain Energy Rev 33:772\u2013781. https:\/\/doi.org\/10.1016\/j.rser.2013.08.055","journal-title":"Renew Sustain Energy Rev"},{"issue":"1","key":"192_CR8","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1007\/s10472-019-09632-y","volume":"86","author":"S Costantini","year":"2019","unstructured":"Costantini S, De Gasperis G, Olivieri R (2019) Digital forensics and investigations meet artificial intelligence. Ann Math Artif Intell 86(1):193\u2013229. https:\/\/doi.org\/10.1007\/s10472-019-09632-y","journal-title":"Ann Math Artif Intell"},{"issue":"1","key":"192_CR9","doi-asserted-by":"publisher","first-page":"482","DOI":"10.1016\/j.jds.2020.05.022","volume":"16","author":"SB Khanagar","year":"2021","unstructured":"Khanagar SB, Al-Ehaideb A, Vishwanathaiah S, Maganur PC, Patil S, Naik S, Baeshen HA, Sarode SS (2021) Scope and performance of artificial intelligence technology in orthodontic diagnosis, treatment planning, and clinical decision-making - a systematic review. J Dental Sci 16(1):482\u2013492. https:\/\/doi.org\/10.1016\/j.jds.2020.05.022","journal-title":"J Dental Sci"},{"key":"192_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.sintl.2021.100128","volume":"2","author":"PP Tumpa","year":"2021","unstructured":"Tumpa PP, Kabir MA (2021) An artificial neural network based detection and classification of melanoma skin cancer using hybrid texture features. Sens Int 2:100128. https:\/\/doi.org\/10.1016\/j.sintl.2021.100128","journal-title":"Sens Int"},{"issue":"2","key":"192_CR11","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1016\/j.survophthal.2018.09.002","volume":"64","author":"R Kapoor","year":"2019","unstructured":"Kapoor R, Walters SP, Al-Aswad LA (2019) The current state of artificial intelligence in ophthalmology. Surv Ophthalmol 64(2):233\u2013240. https:\/\/doi.org\/10.1016\/j.survophthal.2018.09.002","journal-title":"Surv Ophthalmol"},{"key":"192_CR12","doi-asserted-by":"publisher","unstructured":"Bagi Ks, Shreedhara KS (2014) Biometric measurement and classification of IUGR using neural networks. In: 2014 international conference on contemporary computing and informatics (IC3I), pp 157\u2013161. https:\/\/doi.org\/10.1109\/IC3I.2014.7019613","DOI":"10.1109\/IC3I.2014.7019613"},{"issue":"8","key":"192_CR13","doi-asserted-by":"publisher","first-page":"24079","DOI":"10.2196\/24079","volume":"9","author":"NG Poor","year":"2021","unstructured":"Poor NG, West NC, Sreepada RS, Murthy S, G\u00f6rges M (2021) An artificial neural network-based pediatric mortality risk score: development and performance evaluation using data from a large north american registry. JMIR Med Inform 9(8):24079. https:\/\/doi.org\/10.2196\/24079","journal-title":"JMIR Med Inform"},{"issue":"3","key":"192_CR14","doi-asserted-by":"publisher","first-page":"363","DOI":"10.1361\/10599490524002","volume":"14","author":"S Juan-hua","year":"2005","unstructured":"Juan-hua S, He-jun L, Qi-ming D, Ping L, Bu-xi K (2005) Prediction and analysis of the aging properties of rapidly solidified Cu\u2013Cr\u2013Sn\u2013Zn alloy through neural network. J Mater Eng Perform 14(3):363\u2013366. https:\/\/doi.org\/10.1361\/10599490524002","journal-title":"J Mater Eng Perform"},{"key":"192_CR15","doi-asserted-by":"publisher","unstructured":"(2021) Machine learning applications in radiation oncology. Phys Imaging Rad Oncol 19:13\u201324. https:\/\/doi.org\/10.1016\/j.phro.2021.05.007","DOI":"10.1016\/j.phro.2021.05.007"},{"key":"192_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.ultras.2019.105951","volume":"99","author":"C Liu","year":"2019","unstructured":"Liu C, Xie L, Kong W, Lu X, Zhang D, Wu M, Zhang L, Yang B (2019) Prediction of suspicious thyroid nodule using artificial neural network based on radiofrequency ultrasound and conventional ultrasound: a preliminary study. Ultrasonics 99:105951. https:\/\/doi.org\/10.1016\/j.ultras.2019.105951","journal-title":"Ultrasonics"},{"key":"192_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.imu.2021.100655","volume":"26","author":"V Shorewala","year":"2021","unstructured":"Shorewala V (2021) Early detection of coronary heart disease using ensemble techniques. Inform Med Unlock 26:100655. https:\/\/doi.org\/10.1016\/j.imu.2021.100655","journal-title":"Inform Med Unlock"},{"issue":"1","key":"192_CR18","doi-asserted-by":"publisher","first-page":"242","DOI":"10.22266\/ijies2019.0228.24","volume":"12","author":"Y Khourdifi","year":"2019","unstructured":"Khourdifi Y, Bahaj M (2019) Heart disease prediction and classification using machine learning algorithms optimized by particle swarm optimization and ant colony optimization. Int J Intel Eng Syst 12(1):242\u2013252. https:\/\/doi.org\/10.22266\/ijies2019.0228.24","journal-title":"Int J Intel Eng Syst"},{"issue":"2","key":"192_CR19","doi-asserted-by":"publisher","first-page":"190","DOI":"10.1016\/j.jjcc.2011.11.005","volume":"59","author":"OY Atkov","year":"2012","unstructured":"Atkov OY, Gorokhova SG, Sboev AG, Generozov EV, Muraseyeva EV, Moroshkina SY, Cherniy NN (2012) Coronary heart disease diagnosis by artificial neural networks including genetic polymorphisms and clinical parameters. J Cardiol 59(2):190\u2013194. https:\/\/doi.org\/10.1016\/j.jjcc.2011.11.005","journal-title":"J Cardiol"},{"key":"192_CR20","doi-asserted-by":"publisher","unstructured":"Amma NGB (2012) Cardiovascular disease prediction system using genetic algorithm and neural network. In: 2012 international conference on computing, communication and applications, pp 1\u20135. https:\/\/doi.org\/10.1109\/ICCCA.2012.6179185","DOI":"10.1109\/ICCCA.2012.6179185"},{"key":"192_CR21","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1016\/j.asoc.2013.09.020","volume":"14","author":"YE Shao","year":"2014","unstructured":"Shao YE, Hou C-D, Chiu C-C (2014) Hybrid intelligent modeling schemes for heart disease classification. Appl Soft Comput 14:47\u201352. https:\/\/doi.org\/10.1016\/j.asoc.2013.09.020","journal-title":"Appl Soft Comput"},{"key":"192_CR22","doi-asserted-by":"publisher","unstructured":"Feshki MG, Shijani OS (2016) Improving the heart disease diagnosis by evolutionary algorithm of PSO and feed forward neural network. In: 2016 artificial intelligence and robotics (IRANOPEN), pp 48\u201353. https:\/\/doi.org\/10.1109\/RIOS.2016.7529489","DOI":"10.1109\/RIOS.2016.7529489"},{"key":"192_CR23","doi-asserted-by":"publisher","unstructured":"Uyar K, Alhan A (2017) Diagnosis of heart disease using genetic algorithm based trained recurrent fuzzy neural networks. Proc Comput Sci 120:588\u2013593. https:\/\/doi.org\/10.1016\/j.procs.2017.11.283","DOI":"10.1016\/j.procs.2017.11.283"},{"key":"192_CR24","doi-asserted-by":"crossref","unstructured":"Karay\u0131lan T, K\u0131l\u0131\u00e7 \u00d6 (2017) Prediction of heart disease using neural network. In: 2017 international conference on computer science and engineering (UBMK), pp 719\u2013723. IEEE","DOI":"10.1109\/UBMK.2017.8093512"},{"key":"192_CR25","doi-asserted-by":"crossref","unstructured":"Gawande N, Barhatte A (2017) Heart diseases classification using convolutional neural network. In: 2017 2nd international conference on communication and electronics systems (ICCES), pp 17\u201320. IEEE","DOI":"10.1109\/CESYS.2017.8321264"},{"key":"192_CR26","doi-asserted-by":"publisher","unstructured":"Costa W, Figueiredo L, Alves E (2019) Application of an artificial neural network for heart disease diagnosis. In: XXVI Brazilian Congress on Biomedical Engineering, pp 753\u2013758 . Springer. https:\/\/doi.org\/10.1007\/978-981-13-2517-5_115","DOI":"10.1007\/978-981-13-2517-5_115"},{"key":"192_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.imu.2019.100203","volume":"16","author":"CBC Latha","year":"2019","unstructured":"Latha CBC, Jeeva SC (2019) Improving the accuracy of prediction of heart disease risk based on ensemble classification techniques. Inform Med Unlock 16:100203. https:\/\/doi.org\/10.1016\/j.imu.2019.100203","journal-title":"Inform Med Unlock"},{"issue":"1","key":"192_CR28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-020-76635-9","volume":"10","author":"Y Muhammad","year":"2020","unstructured":"Muhammad Y, Tahir M, Hayat M, Chong KT (2020) Early and accurate detection and diagnosis of heart disease using intelligent computational model. Sci Rep 10(1):1\u201317. https:\/\/doi.org\/10.1038\/s41598-020-76635-9","journal-title":"Sci Rep"},{"key":"192_CR29","doi-asserted-by":"crossref","unstructured":"Shihab AN, Mokarrama MJ, Karim R, Khatun S, Arefin MS (2020) An IoT-based heart disease detection system using RNN. In: International conference on image processing and capsule networks, pp 535\u2013545. Springer","DOI":"10.1007\/978-3-030-51859-2_49"},{"key":"192_CR30","unstructured":"Tiwari S (2020) Activation functions in neural networks. https:\/\/www.geeksforgeeks.org\/"},{"key":"192_CR31","doi-asserted-by":"publisher","unstructured":"Duggal R, Gupta A (2017) P-telu: parametric tan hyperbolic linear unit activation for deep neural networks. In: Proceedings of the IEEE international conference on computer vision workshops, pp 974\u2013978. https:\/\/doi.org\/10.1109\/ICCVW.2017.119","DOI":"10.1109\/ICCVW.2017.119"},{"issue":"3","key":"192_CR32","doi-asserted-by":"publisher","first-page":"299","DOI":"10.1016\/0304-3800(89)90035-5","volume":"44","author":"D Wallach","year":"1989","unstructured":"Wallach D, Goffinet B (1989) Mean squared error of prediction as a criterion for evaluating and comparing system models. Ecol Model 44(3):299\u2013306. https:\/\/doi.org\/10.1016\/0304-3800(89)90035-5","journal-title":"Ecol Model"},{"key":"192_CR33","doi-asserted-by":"publisher","unstructured":"Ting KM (2017) In: Sammut C, Webb GI (eds) Confusion matrix. Springer, Boston. https:\/\/doi.org\/10.1007\/978-1-4899-7687-1_50","DOI":"10.1007\/978-1-4899-7687-1_50"},{"key":"192_CR34","doi-asserted-by":"crossref","unstructured":"Davis J, Goadrich M (2006) The relationship between precision-recall and roc curves. In: Proceedings of the 23rd international conference on machine learning, pp 233\u2013240","DOI":"10.1145\/1143844.1143874"}],"container-title":["Journal of Reliable Intelligent Environments"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40860-022-00192-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40860-022-00192-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40860-022-00192-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,4]],"date-time":"2023-03-04T13:10:37Z","timestamp":1677935437000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40860-022-00192-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,12]]},"references-count":34,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2023,3]]}},"alternative-id":["192"],"URL":"https:\/\/doi.org\/10.1007\/s40860-022-00192-3","relation":{},"ISSN":["2199-4668","2199-4676"],"issn-type":[{"value":"2199-4668","type":"print"},{"value":"2199-4676","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,12]]},"assertion":[{"value":"22 June 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 October 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 November 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}