{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T16:06:34Z","timestamp":1759334794975,"version":"build-2065373602"},"publisher-location":"Singapore","reference-count":21,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819509843"},{"type":"electronic","value":"9789819509850"}],"license":[{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"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":[[2026]]},"DOI":"10.1007\/978-981-95-0985-0_10","type":"book-chapter","created":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T22:24:58Z","timestamp":1759271098000},"page":"120-131","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Optimization of Classification Models for Heart Disease: Comparison Between Feature Selection and Dimensionality Reduction Techniques"],"prefix":"10.1007","author":[{"given":"Elisabeth","family":"Restrepo-Parra","sequence":"first","affiliation":[]},{"given":"Paola Patricia","family":"Ariza-Colpas","sequence":"additional","affiliation":[]},{"given":"Laura Victoria Rodr\u00edguez","family":"Restrepo","sequence":"additional","affiliation":[]},{"given":"Marlon Alberto Pi\u00f1eres","family":"Melo","sequence":"additional","affiliation":[]},{"given":"Andrea Camila","family":"Acosta-Solorzano","sequence":"additional","affiliation":[]},{"given":"Miguel Alberto","family":"Urina-Triana","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,10,1]]},"reference":[{"issue":"3","key":"10_CR1","doi-asserted-by":"publisher","first-page":"524","DOI":"10.3390\/iot5030024","volume":"5","author":"E Restrepo-Parra","year":"2024","unstructured":"Restrepo-Parra, E., Ariza-Colpas, P.P., Torres-Bonilla, L.V., Pi\u00f1eres-Melo, M.A., Urina-Triana, M.A., Butt-Aziz, S.: Home monitoring tools to support tracking patients with cardio-cerebrovascular diseases: scientometric review. IoT 5(3), 524\u2013559 (2024)","journal-title":"IoT"},{"key":"10_CR2","doi-asserted-by":"publisher","first-page":"411","DOI":"10.1016\/j.compbiomed.2018.09.009","volume":"102","author":"\u00d6 Y\u0131ld\u0131r\u0131m","year":"2018","unstructured":"Y\u0131ld\u0131r\u0131m, \u00d6., P\u0142awiak, P., Tan, R.S., Acharya, U.R.: Arrhythmia detection using deep convolutional neural network with long duration ECG signals. Comput. Biol. Med. 102, 411\u2013420 (2018)","journal-title":"Comput. Biol. Med."},{"key":"10_CR3","doi-asserted-by":"crossref","unstructured":"Mortazavi, B. J., et al.: Analysis of machine learning techniques for heart failure readmissions.\u00a0Circulation: Cardiovascular Quality and Outcomes,\u00a09(6), 629\u2013640 (2016)","DOI":"10.1161\/CIRCOUTCOMES.116.003039"},{"issue":"8","key":"10_CR4","doi-asserted-by":"publisher","first-page":"581","DOI":"10.1038\/s41569-021-00522-7","volume":"18","author":"K Bayoumy","year":"2021","unstructured":"Bayoumy, K., et al.: Smart wearable devices in cardiovascular care: where we are and how to move forward. Nat. Rev. Cardiol. 18(8), 581\u2013599 (2021)","journal-title":"Nat. Rev. Cardiol."},{"issue":"1","key":"10_CR5","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1089\/tmj.2016.0051","volume":"23","author":"A Vegesna","year":"2017","unstructured":"Vegesna, A., Tran, M., Angelaccio, M., Arcona, S.: Remote patient monitoring via non-invasive digital technologies: a systematic review. Telemed. e-Health 23(1), 3\u201317 (2017)","journal-title":"Telemed. e-Health"},{"issue":"11","key":"10_CR6","doi-asserted-by":"publisher","DOI":"10.1161\/JAHA.115.002239","volume":"4","author":"SS Martin","year":"2015","unstructured":"Martin, S.S., et al.: Mactive: a randomized clinical trial of an automated mHealth intervention for physical activity promotion. J. Am. Heart Assoc. 4(11), e002239 (2015)","journal-title":"J. Am. Heart Assoc."},{"issue":"1","key":"10_CR7","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1001\/jamacardio.2016.4395","volume":"2","author":"MV McConnell","year":"2017","unstructured":"McConnell, M.V., et al.: Feasibility of obtaining measures of lifestyle from a smartphone app: the myheart counts cardiovascular health study. JAMA Cardiology 2(1), 67\u201376 (2017)","journal-title":"JAMA Cardiology"},{"issue":"6","key":"10_CR8","doi-asserted-by":"publisher","first-page":"877","DOI":"10.1007\/s00125-019-4864-7","volume":"62","author":"R Shan","year":"2019","unstructured":"Shan, R., Sarkar, S., Martin, S.S.: Digital health technology and mobile devices for the management of diabetes mellitus: state of the art. Diabetologia 62(6), 877\u2013887 (2019)","journal-title":"Diabetologia"},{"key":"10_CR9","doi-asserted-by":"publisher","first-page":"280","DOI":"10.1016\/j.ijcard.2019.05.066","volume":"292","author":"B Smith","year":"2019","unstructured":"Smith, B., Magnani, J.W.: New technologies, new disparities: the intersection of electronic health and digital health literacy. Int. J. Cardiol. 292, 280\u2013282 (2019)","journal-title":"Int. J. Cardiol."},{"key":"10_CR10","doi-asserted-by":"publisher","unstructured":"Alizadehsani, R., Roshanzamir, M., Sani, Z.: Z-Alizadeh Sani. UCI Machine Learning Repository. https:\/\/doi.org\/10.24432\/C5Q31T (https:\/\/archive.ics.uci.edu\/dataset\/412\/z+alizadeh+sani)(2017)","DOI":"10.24432\/C5Q31T"},{"key":"10_CR11","doi-asserted-by":"crossref","unstructured":"LaValley, M.P.: Logistic regression.\u00a0Circulation\u00a0117(18), 2395-2399 (2008).","DOI":"10.1161\/CIRCULATIONAHA.106.682658"},{"issue":"9","key":"10_CR12","doi-asserted-by":"publisher","first-page":"1011","DOI":"10.1038\/nbt0908-1011","volume":"26","author":"C Kingsford","year":"2008","unstructured":"Kingsford, C., Salzberg, S.L.: What are decision trees? Nat. Biotechnol. 26(9), 1011\u20131013 (2008)","journal-title":"Nat. Biotechnol."},{"issue":"1","key":"10_CR13","doi-asserted-by":"publisher","first-page":"31","DOI":"10.17849\/insm-47-01-31-39.1","volume":"47","author":"SJ Rigatti","year":"2017","unstructured":"Rigatti, S.J.: Random forest. J. Insur. Med. 47(1), 31\u201339 (2017)","journal-title":"J. Insur. Med."},{"key":"10_CR14","doi-asserted-by":"crossref","unstructured":"Schapire, R.E.: Explaining adaboost. In:\u00a0Empirical inference: festschrift in honor of Vladimir, N., Vapnik, pp. 37\u201352. Berlin, Heidelberg: Springer Berlin Heidelberg (2013).","DOI":"10.1007\/978-3-642-41136-6_5"},{"issue":"10","key":"10_CR15","first-page":"53","volume":"14","author":"OI Sheluhin","year":"2020","unstructured":"Sheluhin, O.I., Ivannikova, V.P.: Comparative analysis of informative features quantity and composition selection methods for the computer attacks classification using the unsw-nb15 dataset. T-Comm-\u0422\u0435\u043b\u0435\u043a\u043e\u043c\u043c\u0443\u043d\u0438\u043a\u0430\u0446\u0438\u0438 \u0438 \u0422\u0440\u0430\u043d\u0441\u043f\u043e\u0440\u0442 14(10), 53\u201360 (2020)","journal-title":"T-Comm-\u0422\u0435\u043b\u0435\u043a\u043e\u043c\u043c\u0443\u043d\u0438\u043a\u0430\u0446\u0438\u0438 \u0438 \u0422\u0440\u0430\u043d\u0441\u043f\u043e\u0440\u0442"},{"key":"10_CR16","doi-asserted-by":"crossref","unstructured":"Paria, A., Ghatak, A., Jana, B., Adhikary, A., Sen, P., Das, R.: ClimateCast: ML-powered temperature forecasting for north 24 parganas. In\u00a02024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Application (ICAIQSA), pp. 1\u20136. IEEE (2024)","DOI":"10.1109\/ICAIQSA64000.2024.10882442"},{"issue":"9","key":"10_CR17","doi-asserted-by":"publisher","first-page":"3211","DOI":"10.3390\/app10093211","volume":"10","author":"H Jeon","year":"2020","unstructured":"Jeon, H., Oh, S.: Hybrid-recursive feature elimination for efficient feature selection. Appl. Sci. 10(9), 3211 (2020)","journal-title":"Appl. Sci."},{"issue":"3","key":"10_CR18","first-page":"173","volume":"4","author":"S Karamizadeh","year":"2013","unstructured":"Karamizadeh, S., Abdullah, S.M., Manaf, A.A., Zamani, M., Hooman, A.: An overview of principal component analysis. J. Sign. Inf. Process. 4(3), 173\u2013175 (2013)","journal-title":"J. Sign. Inf. Process."},{"key":"10_CR19","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1613\/jair.953","volume":"16","author":"NV Chawla","year":"2002","unstructured":"Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. J. Artifi. Intell. Res. 16, 321\u2013357 (2002)","journal-title":"J. Artifi. Intell. Res."},{"key":"10_CR20","doi-asserted-by":"crossref","unstructured":"Hasanin, T., Khoshgoftaar, T.: The effects of random undersampling with simulated class imbalance for big data. In:\u00a02018 IEEE International Conference on Information Reuse And Integration (IRI), pp. 70\u201379. IEEE (2018).","DOI":"10.1109\/IRI.2018.00018"},{"key":"10_CR21","unstructured":"Calabria-Sarmiento, J.C., et al.: Software applications to health sector: a systematic review of literature (2018)."}],"container-title":["Lecture Notes in Computer Science","Advances in Swarm Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-0985-0_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,30]],"date-time":"2025-09-30T22:25:03Z","timestamp":1759271103000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-0985-0_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,1]]},"ISBN":["9789819509843","9789819509850"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-0985-0_10","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025,10,1]]},"assertion":[{"value":"1 October 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICSI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Swarm Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Yokohama","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Japan","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 July 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 July 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"swarm2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.iasei.org\/icsi2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}