{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T12:48:46Z","timestamp":1740142126047,"version":"3.37.3"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,12,1]],"date-time":"2022-12-01T00:00:00Z","timestamp":1669852800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,12,1]],"date-time":"2022-12-01T00:00:00Z","timestamp":1669852800000},"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":["Innovations Syst Softw Eng"],"published-print":{"date-parts":[[2023,3]]},"DOI":"10.1007\/s11334-022-00512-z","type":"journal-article","created":{"date-parts":[[2022,12,1]],"date-time":"2022-12-01T21:15:22Z","timestamp":1669929322000},"page":"33-46","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A hybrid approach for medical images classification and segmentation to reduce complexity"],"prefix":"10.1007","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7945-4616","authenticated-orcid":false,"given":"Ankit","family":"Kumar","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Surbhi","family":"Bhatia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rajat","family":"Bhardwaj","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kamred Udham","family":"Singh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Neeraj","family":"varshney","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Linesh","family":"Raja","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,12,1]]},"reference":[{"issue":"3","key":"512_CR1","doi-asserted-by":"publisher","first-page":"2032","DOI":"10.1109\/TII.2021.3098306","volume":"18","author":"X Yuan","year":"2022","unstructured":"Yuan X, Chen J, Zhang K, Wu Y, Yang T (2022) A stable AI-based binary and multiple class heart disease prediction model for IoMT. IEEE Trans Industr Inf 18(3):2032\u20132040. https:\/\/doi.org\/10.1109\/TII.2021.3098306","journal-title":"IEEE Trans Industr Inf"},{"issue":"10","key":"512_CR2","doi-asserted-by":"publisher","first-page":"1180","DOI":"10.1080\/10255842.2022.2034795","volume":"25","author":"A Sheeba","year":"2022","unstructured":"Sheeba A, Padmakala S, Subasini CA, Karuppiah SP (2022) MKELM: Mixed kernel extreme learning machine using BMDA optimization for web services based heart disease prediction in smart healthcare. Comput Methods Biomech Biomed Eng 25(10):1180\u20131194. https:\/\/doi.org\/10.1080\/10255842.2022.2034795","journal-title":"Comput Methods Biomech Biomed Eng"},{"key":"512_CR3","doi-asserted-by":"publisher","DOI":"10.1177\/1063293X221125231","author":"K Dhasaradhan","year":"2022","unstructured":"Dhasaradhan K, Jaichandran R (2022) Performance analysis of machine learning algorithms in heart disease prediction. Concurr Eng-Res Appl. https:\/\/doi.org\/10.1177\/1063293X221125231","journal-title":"Concurr Eng-Res Appl"},{"issue":"9","key":"512_CR4","doi-asserted-by":"publisher","first-page":"178","DOI":"10.3991\/ijoe.v18i09.30801","volume":"18","author":"SAH Fazlur","year":"2022","unstructured":"Fazlur SAH, Thillaigovindan SK (2022) Integrated deep learning model for heart disease prediction using variant medical data sets. Int J Online Biomed Eng 18(9):178\u2013191. https:\/\/doi.org\/10.3991\/ijoe.v18i09.30801","journal-title":"Int J Online Biomed Eng"},{"issue":"2","key":"512_CR5","doi-asserted-by":"publisher","first-page":"210","DOI":"10.1161\/CIRCRESAHA.121.320598","volume":"130","author":"S Shea","year":"2022","unstructured":"Shea S, Blaha MJ (2022) Long-term risk prediction for heart failure, disparities, and early prevention. Circ Res 130(2):210\u2013212. https:\/\/doi.org\/10.1161\/CIRCRESAHA.121.320598","journal-title":"Circ Res"},{"key":"512_CR6","doi-asserted-by":"publisher","DOI":"10.3390\/jpm12081208","author":"A Al Bataineh","year":"2022","unstructured":"Al Bataineh A, Manacek S (2022) MLP-PSO hybrid algorithm for heart disease prediction. J Personal Med. https:\/\/doi.org\/10.3390\/jpm12081208","journal-title":"J Personal Med"},{"issue":"13","key":"512_CR7","doi-asserted-by":"publisher","first-page":"18155","DOI":"10.1007\/s11042-022-12425-x","volume":"81","author":"MG El-Shafiey","year":"2022","unstructured":"El-Shafiey MG, Hagag A, El-Dahshan E-SA, Ismail MA (2022) A hybrid GA and PSO optimized approach for heart-disease prediction based on random forest. Multimed Tools Appl 81(13):18155\u201318179. https:\/\/doi.org\/10.1007\/s11042-022-12425-x","journal-title":"Multimed Tools Appl"},{"issue":"4, S","key":"512_CR8","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1016\/j.healun.2022.01.1102","volume":"41","author":"P Kampaltsis","year":"2022","unstructured":"Kampaltsis P, Emfiezoglou M, Siouras A, Eynde J, Moustakidis S, Doulamis IP, Duque ER, Briasoulis A (2022) Prediction of 1-year mortality after heart transplantation in adults with congenital heart disease with machine learning models. J Heart Lung Transpl 41(4, S):436","journal-title":"J Heart Lung Transpl"},{"key":"512_CR9","doi-asserted-by":"publisher","DOI":"10.1142\/S0219265921450031","author":"SS Amer","year":"2022","unstructured":"Amer SS, Wander G, Singh M, Bahsoon R, Jennings NR, Gill SS (2022) Biolearner: a machine learning-powered smart heart disease risk prediction system utilizing biomedical markers. J Interconnect Netw. https:\/\/doi.org\/10.1142\/S0219265921450031","journal-title":"J Interconnect Netw"},{"issue":"6","key":"512_CR10","doi-asserted-by":"publisher","first-page":"1410","DOI":"10.1016\/j.ekir.2022.04.004","volume":"7","author":"M Mete","year":"2022","unstructured":"Mete M, Ayvaci MUS, Ariyamuthu VK, Amin A, Peltz M, Thibodeau JT, Grodin JL, Mammen PPA, Garg S, Araj F, Morlend R, Drazner MH, AbdulRahim N, Kim Y, Salam Y, Gungor AB, Delibasi B, Kotla SK, MacConmara MP, Anand PM, Gupta G, Tanriover B (2022) Predicting post-heart transplant composite renal outcome risk in adults: a machine learning decision tool. Kidney Int Rep 7(6):1410\u20131415. https:\/\/doi.org\/10.1016\/j.ekir.2022.04.004","journal-title":"Kidney Int Rep"},{"key":"512_CR11","doi-asserted-by":"publisher","DOI":"10.1142\/S0219519422500518","author":"PR Kumar","year":"2022","unstructured":"Kumar PR, Ravichandran S, Narayana S (2022) Optimization assisted hybrid intelligent system for heart disease prediction. J Mech Med Biol. https:\/\/doi.org\/10.1142\/S0219519422500518","journal-title":"J Mech Med Biol"},{"key":"512_CR12","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-022-07527-4","author":"VE Jesi","year":"2022","unstructured":"Jesi VE, Aslam SM (2022) An intelligent disease prediction and monitoring system using feature selection, multi-neural network and fuzzy rules. Neural Comput Appl. https:\/\/doi.org\/10.1007\/s00521-022-07527-4","journal-title":"Neural Comput Appl"},{"key":"512_CR13","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-022-07064-0","author":"P Dileep","year":"2022","unstructured":"Dileep P, Rao KN, Bodapati P, Gokuruboyina S, Peddi R, Grover A, Sheetal A (2022) An automatic heart disease prediction using cluster-based bi-directional D(C-BiLSTM) algorithm. Neural Comput Appl. https:\/\/doi.org\/10.1007\/s00521-022-07064-0","journal-title":"Neural Comput Appl"},{"issue":"4","key":"512_CR14","doi-asserted-by":"publisher","first-page":"334","DOI":"10.1504\/IJBET.2022.123149","volume":"38","author":"N Masih","year":"2022","unstructured":"Masih N, Ahuja S (2022) Application of data mining techniques for early detection of heart diseases using Famingham heart study dataset. Int J Biomed Eng Technol 38(4):334\u2013344","journal-title":"Int J Biomed Eng Technol"},{"issue":"Suppl 1,1","key":"512_CR15","first-page":"519","volume":"30","author":"B Sedaghati-Khayat","year":"2022","unstructured":"Sedaghati-Khayat B, Bos M, Tan J, van Meurs JBJ, Uitterlinden A, Hajek C, Rotter JI, Kavousi M, van Rooij J (2022) Polygenic risk prediction ability of gender-stratified coronary heart disease. Eur J Hum Genet 30(Suppl 1,1):519","journal-title":"Eur J Hum Genet"},{"key":"512_CR16","doi-asserted-by":"publisher","DOI":"10.3390\/healthcare10061137","author":"N Absar","year":"2022","unstructured":"Absar N, Das EK, Shoma SN, Khandaker MU, Miraz MH, Faruque MRI, Tamam N, Sulieman A, Pathan RK (2022) The efficacy of machine-learning-supported smart system for heart disease prediction. Healthcare. https:\/\/doi.org\/10.3390\/healthcare10061137","journal-title":"Healthcare"},{"issue":"8","key":"512_CR17","doi-asserted-by":"publisher","first-page":"892","DOI":"10.1097\/GME.0000000000002038","volume":"29","author":"DR Black","year":"2022","unstructured":"Black DR (2022) A new tool in the prediction of cardiovascular disease? perhaps. Menopause J North Am Menopause Soc 29(8):892\u2013893. https:\/\/doi.org\/10.1097\/GME.0000000000002038","journal-title":"Menopause J North Am Menopause Soc"},{"key":"512_CR18","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/7529472","author":"K Phasinam","year":"2022","unstructured":"Phasinam K, Mondal T, Novaliendry D, Yang C-H, Dutta C, Shabaz M (2022) Analyzing the performance of machine learning techniques in disease prediction. J Food Qual. https:\/\/doi.org\/10.1155\/2022\/7529472","journal-title":"J Food Qual"},{"issue":"2","key":"512_CR19","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1097\/PCC.0000000000002875","volume":"23","author":"JJ Elhoff","year":"2022","unstructured":"Elhoff JJ (2022) Do we need to reframe our approach to short-term outcomes in the cardiac ICU? Pediatr Crit Care Med 23(2):140\u2013143. https:\/\/doi.org\/10.1097\/PCC.0000000000002875","journal-title":"Pediatr Crit Care Med"},{"key":"512_CR20","doi-asserted-by":"publisher","DOI":"10.1186\/s40959-022-00133-2","author":"S Patil","year":"2022","unstructured":"Patil S, Pingle S-R, Shalaby K, Kim AS (2022) Mediastinal irradiation and valvular heart disease. Cardio-Oncol. https:\/\/doi.org\/10.1186\/s40959-022-00133-2","journal-title":"Cardio-Oncol"},{"key":"512_CR21","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/6517716","author":"K Karthick","year":"2022","unstructured":"Karthick K, Aruna SK, Samikannu R, Kuppusamy R, Teekaraman Y, Thelkar AR (2022) Implementation of a heart disease risk prediction model using machine learning. Comput Math Methods Med. https:\/\/doi.org\/10.1155\/2022\/6517716","journal-title":"Comput Math Methods Med"},{"key":"512_CR22","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/5901445","author":"J Bai","year":"2022","unstructured":"Bai J, Fu J, Du S, Chen X, Zhang C (2022) Prediction of heart failure in children with congenital heart disease based on multichannel LSTM. Mob Inf Syst. https:\/\/doi.org\/10.1155\/2022\/5901445","journal-title":"Mob Inf Syst"},{"issue":"3","key":"512_CR23","doi-asserted-by":"publisher","first-page":"501","DOI":"10.1016\/j.jcmg.2021.11.004","volume":"15","author":"MH Criqui","year":"2022","unstructured":"Criqui MH, Bhatia HS (2022) How should we measure and score coronary artery calcium? JACC-Cardiovasc Imaging 15(3):501\u2013503","journal-title":"JACC-Cardiovasc Imaging"},{"issue":"7","key":"512_CR24","first-page":"810","volume":"13","author":"N Alotaibi","year":"2022","unstructured":"Alotaibi N, Alzahrani M (2022) Comparative analysis of machine learning algorithms and data mining techniques for predicting the existence of heart disease. Int J Adv Comput Sci Appl 13(7):810\u2013818","journal-title":"Int J Adv Comput Sci Appl"},{"issue":"2","key":"512_CR25","doi-asserted-by":"publisher","first-page":"3195","DOI":"10.32604\/cmc.2022.026064","volume":"72","author":"J Rashid","year":"2022","unstructured":"Rashid J, Kanwal S, Kim J, Nisar MW, Naseem U, Hussain A (2022) Heart disease diagnosis using the brute force algorithm and machine learning techniques. CMC-Comput Mater Contin 72(2):3195\u20133211. https:\/\/doi.org\/10.32604\/cmc.2022.026064","journal-title":"CMC-Comput Mater Contin"},{"key":"512_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.medengphy.2022.103825","author":"J Azmi","year":"2022","unstructured":"Azmi J, Arif M, Nafis MT, Alam MA, Tanweer S, Wang G (2022) A systematic review on machine learning approaches for cardiovascular disease prediction using medical big data. Med Eng Phys. https:\/\/doi.org\/10.1016\/j.medengphy.2022.103825","journal-title":"Med Eng Phys"},{"issue":"12","key":"512_CR27","doi-asserted-by":"publisher","first-page":"1409","DOI":"10.1080\/10255842.2022.2078966","volume":"25","author":"D Deepika","year":"2022","unstructured":"Deepika D, Balaji N (2022) Effective heart disease prediction with grey-wolf with firefly algorithm-differential evolution (GF-DE) for feature selection and weighted ANN classification. Comput Methods Biomech Biomed Eng 25(12):1409\u20131427. https:\/\/doi.org\/10.1080\/10255842.2022.2078966","journal-title":"Comput Methods Biomech Biomed Eng"},{"key":"512_CR28","doi-asserted-by":"publisher","DOI":"10.3390\/app12157449","author":"GN Ahmad","year":"2022","unstructured":"Ahmad GN, Shafiullah, Fatima H, Abbas M, Rahman O, Imdadullah, Alqahtani MS (2022) Mixed machine learning approach for efficient prediction of human heart disease by identifying the numerical and categorical features. Appl Sci-Basel. https:\/\/doi.org\/10.3390\/app12157449","journal-title":"Appl Sci-Basel"},{"key":"512_CR29","doi-asserted-by":"publisher","DOI":"10.1080\/10255842.2021.2013828","author":"VM Ganesh","year":"2022","unstructured":"Ganesh VM, Nithiyanantham J (2022) Heuristic-based channel selection with enhanced deep learning for heart disease prediction under WBAN. Comput Methods Biomech Biomed Eng. https:\/\/doi.org\/10.1080\/10255842.2021.2013828","journal-title":"Comput Methods Biomech Biomed Eng"},{"key":"512_CR30","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/9580896","author":"SI Ansarullah","year":"2022","unstructured":"Ansarullah SI, Saif SM, Kumar P, Kirmani MM (2022) Significance of visible non-invasive risk attributes for the initial prediction of heart disease using different machine learning techniques. Comput Intell Neurosci. https:\/\/doi.org\/10.1155\/2022\/9580896","journal-title":"Comput Intell Neurosci"},{"key":"512_CR31","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/9797844","author":"KS Archana","year":"2022","unstructured":"Archana KS, Sivakumar B, Kuppusamy R, Teekaraman Y, Radhakrishnan A (2022) Automated cardioailment identification and prevention by hybrid machine learning models. Comput Math Methods Med. https:\/\/doi.org\/10.1155\/2022\/9797844","journal-title":"Comput Math Methods Med"},{"issue":"2","key":"512_CR32","doi-asserted-by":"publisher","first-page":"677","DOI":"10.32604\/cmes.2022.018580","volume":"31","author":"S Kannan","year":"2022","unstructured":"Kannan S (2022) Modelling an efficient clinical decision support system for heart disease prediction using learning and optimization approaches. CMES-Comput Model Eng Sci 31(2):677\u2013694. https:\/\/doi.org\/10.32604\/cmes.2022.018580","journal-title":"CMES-Comput Model Eng Sci"},{"issue":"4","key":"512_CR33","doi-asserted-by":"publisher","first-page":"551","DOI":"10.1016\/j.cardfail.2021.11.019","volume":"28","author":"C Maulion","year":"2022","unstructured":"Maulion C, Januzzi JL (2022) Risk prediction scores in cardiovascular disease: Useful tool or \u201cmodel of the week\u2019\u2019? J Cardiac Fail 28(4):551\u2013553. https:\/\/doi.org\/10.1016\/j.cardfail.2021.11.019","journal-title":"J Cardiac Fail"},{"key":"512_CR34","doi-asserted-by":"publisher","unstructured":"Manimurugan S, Almutairi S, Aborokbah MM, Narmatha C, Ganesan S, Chilamkurti N, Alzaheb RA, Almoamari H (2022) Two-stage classification model for the prediction of heart disease using IoMT and artificial intelligence. Sensors. https:\/\/doi.org\/10.3390\/s22020476","DOI":"10.3390\/s22020476"},{"key":"512_CR35","doi-asserted-by":"publisher","DOI":"10.3389\/fcvm.2022.995234","author":"P Leeson","year":"2022","unstructured":"Leeson P, Nanayakkara S, Lamata P (2022) Editorial: translating artificial intelligence into clinical use within cardiology. Front Cardiovasc Med. https:\/\/doi.org\/10.3389\/fcvm.2022.995234","journal-title":"Front Cardiovasc Med"},{"key":"512_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2022.103666","author":"C Pan","year":"2022","unstructured":"Pan C, Poddar A, Mukherjee R, Ray AK (2022) Impact of categorical and numerical features in ensemble machine learning frameworks for heart disease prediction. Biomed Signal Process Control. https:\/\/doi.org\/10.1016\/j.bspc.2022.103666","journal-title":"Biomed Signal Process Control"}],"container-title":["Innovations in Systems and Software Engineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11334-022-00512-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11334-022-00512-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11334-022-00512-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,25]],"date-time":"2023-03-25T17:02:42Z","timestamp":1679763762000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11334-022-00512-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,1]]},"references-count":36,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2023,3]]}},"alternative-id":["512"],"URL":"https:\/\/doi.org\/10.1007\/s11334-022-00512-z","relation":{},"ISSN":["1614-5046","1614-5054"],"issn-type":[{"type":"print","value":"1614-5046"},{"type":"electronic","value":"1614-5054"}],"subject":[],"published":{"date-parts":[[2022,12,1]]},"assertion":[{"value":"20 June 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 November 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 December 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}