{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T16:57:32Z","timestamp":1761929852540,"version":"3.41.0"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783319574530"},{"type":"electronic","value":"9783319574547"}],"license":[{"start":{"date-parts":[[2017,1,1]],"date-time":"2017-01-01T00:00:00Z","timestamp":1483228800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017]]},"DOI":"10.1007\/978-3-319-57454-7_51","type":"book-chapter","created":{"date-parts":[[2017,4,22]],"date-time":"2017-04-22T12:09:31Z","timestamp":1492862971000},"page":"654-670","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["A Fast Fourier Transform-Coupled Machine Learning-Based Ensemble Model for Disease Risk Prediction Using a Real-Life Dataset"],"prefix":"10.1007","author":[{"given":"Raid","family":"Lafta","sequence":"first","affiliation":[]},{"given":"Ji","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Xiaohui","family":"Tao","sequence":"additional","affiliation":[]},{"given":"Yan","family":"Li","sequence":"additional","affiliation":[]},{"given":"Wessam","family":"Abbas","sequence":"additional","affiliation":[]},{"given":"Yonglong","family":"Luo","sequence":"additional","affiliation":[]},{"given":"Fulong","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Vincent S.","family":"Tseng","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2017,4,23]]},"reference":[{"key":"51_CR1","doi-asserted-by":"publisher","DOI":"10.1093\/acprof:oso\/9780198578154.001.0001","volume-title":"A Life Course Approach to Chronic Disease Epidemiology","author":"D Kuh","year":"2004","unstructured":"Kuh, D., Shlomo, Y.B.: A Life Course Approach to Chronic Disease Epidemiology. Inem Oxford University Press, London (2004)"},{"key":"51_CR2","volume-title":"International Diabetes Federation Diabetes Atlas","author":"ID Atlas","year":"2013","unstructured":"Atlas, I.D.: International Diabetes Federation Diabetes Atlas, 6th edn. International Diabetes Federation, Basel (2013)","edition":"6"},{"issue":"7","key":"51_CR3","doi-asserted-by":"publisher","first-page":"3682","DOI":"10.1016\/j.eswa.2014.12.042","volume":"42","author":"NT Thong","year":"2015","unstructured":"Thong, N.T.: HIFCF: an effective hybrid model between picture fuzzy clustering and intuitionistic fuzzy recommender systems for medical diagnosis. Expert Syst. Appl. 42(7), 3682\u20133701 (2015)","journal-title":"Expert Syst. Appl."},{"issue":"9","key":"51_CR4","doi-asserted-by":"publisher","first-page":"202","DOI":"10.1007\/s10916-016-0560-z","volume":"40","author":"D Chen","year":"2016","unstructured":"Chen, D., Jin, D., Goh, T.-T., Li, N., Wei, L.: Context-awareness based personalized recommendation of anti-hypertension drugs. J. Med. Syst. 40(9), 202 (2016)","journal-title":"J. Med. Syst."},{"key":"51_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/3-540-45808-5_1","volume-title":"Neural Nets","author":"G Valentini","year":"2002","unstructured":"Valentini, G., Masulli, F.: Ensembles of learning machines. In: Marinaro, M., Tagliaferri, R. (eds.) WIRN 2002. LNCS, vol. 2486, pp. 3\u201320. Springer, Heidelberg (2002). doi:10.1007\/3-540-45808-5_1"},{"issue":"2","key":"51_CR6","first-page":"123","volume":"24","author":"L Breiman","year":"1996","unstructured":"Breiman, L.: Bagging predictors. Mach. Learn. 24(2), 123\u2013140 (1996)","journal-title":"Mach. Learn."},{"issue":"4","key":"51_CR7","doi-asserted-by":"publisher","first-page":"7675","DOI":"10.1016\/j.eswa.2008.09.013","volume":"36","author":"R Das","year":"2009","unstructured":"Das, R., Turkoglu, I., Sengur, A.: Effective diagnosis of heart disease through neural networks ensembles. Expert Syst. Appl. 36(4), 7675\u20137680 (2009)","journal-title":"Expert Syst. Appl."},{"issue":"6","key":"51_CR8","doi-asserted-by":"publisher","first-page":"1379","DOI":"10.1007\/s13369-013-0588-z","volume":"38","author":"T Helmy","year":"2013","unstructured":"Helmy, T., Rahman, S., Hossain, M.I., Abdelraheem, A.: Non-linear heterogeneous ensemble model for permeability prediction of oil reservoirs. Arab. J. Sci. Eng. 38(6), 1379\u20131395 (2013)","journal-title":"Arab. J. Sci. Eng."},{"issue":"2","key":"51_CR9","doi-asserted-by":"publisher","first-page":"305","DOI":"10.1007\/s13246-015-0337-6","volume":"38","author":"S Bashir","year":"2015","unstructured":"Bashir, S., Qamar, U., Khan, F.H.: BagMOOV: a novel ensemble for heart disease prediction bootstrap aggregation with multi-objective optimized voting. Australas. Phys. Eng. Sci. Med. 38(2), 305\u2013323 (2015)","journal-title":"Australas. Phys. Eng. Sci. Med."},{"issue":"7","key":"51_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10916-016-0536-z","volume":"40","author":"L Verma","year":"2016","unstructured":"Verma, L., Srivastava, S., Negi, P.: A hybrid data mining model to predict coronary artery disease cases using non-invasive clinical data. J. Med. Syst. 40(7), 1\u20137 (2016)","journal-title":"J. Med. Syst."},{"key":"51_CR11","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1016\/j.enbuild.2016.05.083","volume":"127","author":"C-L Tsai","year":"2016","unstructured":"Tsai, C.-L., Chen, W.T., Chang, C.-S.: Polynomial-Fourier series model for analyzing and predicting electricity consumption in buildings. Energy Build. 127, 301\u2013312 (2016)","journal-title":"Energy Build."},{"key":"51_CR12","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1016\/j.enbuild.2015.03.048","volume":"97","author":"Y Ji","year":"2015","unstructured":"Ji, Y., Xu, P., Ye, Y.: HVAC terminal hourly end-use disaggregation in commercial buildings with Fourier series model. Energy Build. 97, 33\u201346 (2015)","journal-title":"Energy Build."},{"key":"51_CR13","doi-asserted-by":"publisher","first-page":"532","DOI":"10.1016\/j.cam.2016.02.009","volume":"309","author":"BM Brentan","year":"2016","unstructured":"Brentan, B.M., Luvizotto Jr., E., Herrera, M., Izquierdo, J., Prez-Garca, R.: Hybrid regression model for near real-time urban water demand forecasting. J. Comput. Appl. Math. 309, 532\u2013541 (2016)","journal-title":"J. Comput. Appl. Math."},{"issue":"3","key":"51_CR14","doi-asserted-by":"publisher","first-page":"245","DOI":"10.1061\/(ASCE)WR.1943-5452.0000177","volume":"138","author":"FK Odan","year":"2012","unstructured":"Odan, F.K., Reis, L.F.R.: Hybrid water demand forecasting model associating artificial neural network with Fourier series. J. Water Resour. Plan. Manag. 138(3), 245\u2013256 (2012)","journal-title":"J. Water Resour. Plan. Manag."},{"issue":"2","key":"51_CR15","doi-asserted-by":"publisher","first-page":"541","DOI":"10.1109\/TBME.2014.2360101","volume":"62","author":"K Samiee","year":"2015","unstructured":"Samiee, K., Kovcs, P., Gabbouj, M.: Epileptic seizure classification of EEG time-series using rational discrete short-time Fourier transform. IEEE Trans. Biomed. Eng. 62(2), 541\u2013552 (2015)","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"51_CR16","doi-asserted-by":"crossref","unstructured":"Kovacs, P., Samiee, K., Gabbouj, M.: On application of rational discrete short time Fourier transform in epileptic seizure classification. IEEE Trans. Biomed. Eng. 5839\u20135843 (2014)","DOI":"10.1109\/ICASSP.2014.6854723"},{"issue":"3","key":"51_CR17","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1023\/A:1018628609742","volume":"9","author":"JA Suykens","year":"1999","unstructured":"Suykens, J.A., Vandewalle, J.: Least squares support vector machine classifiers. Neural Process. Lett. 9(3), 293\u2013300 (1999)","journal-title":"Neural Process. Lett."},{"issue":"4","key":"51_CR18","doi-asserted-by":"publisher","first-page":"850","DOI":"10.1007\/s10878-015-9848-z","volume":"30","author":"Y Bai","year":"2015","unstructured":"Bai, Y., Han, X., Chen, T., Yu, H.: Quadratic kernel-free least squares support vector machine for target diseases classification. J. Comb. Optim. 30(4), 850\u2013870 (2015)","journal-title":"J. Comb. Optim."},{"key":"51_CR19","doi-asserted-by":"crossref","unstructured":"Sharawardi, N.A., Choo, Y.-H., Chong, S.-H., Muda, A.K., Goh, O.S.: Single channel sEMG muscle fatigue prediction: an implementation using least square support vector machine. In: Information and Communication Technologies (WICT), pp. 320\u2013325 (2014)","DOI":"10.1109\/WICT.2014.7077287"},{"issue":"7","key":"51_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10916-016-0517-2","volume":"40","author":"S Li","year":"2016","unstructured":"Li, S., Tang, B., He, H.: An imbalanced learning based MDR-TB early warning system. J. Med. Syst. 40(7), 1\u20139 (2016)","journal-title":"J. Med. Syst."},{"key":"51_CR21","unstructured":"Gao, H., Jian, S., Peng, Y., Liu, X.: A subspace ensemble framework for classification with high dimensional missing data. Multidimens. Syst. Sig. Process. 1\u201316 (2016)"},{"key":"51_CR22","volume-title":"Data Mining: Concepts and Techniques","author":"J Han","year":"2011","unstructured":"Han, J., Kamber, M., Pei, J.: Data Mining: Concepts and Techniques. Elsevier, Amsterdam (2011)"},{"key":"51_CR23","volume-title":"Signal Analysis Wavelets, Filter Banks, Time-Frequency Transforms and Applications","author":"M Alfred","year":"1999","unstructured":"Alfred, M.: Signal Analysis Wavelets, Filter Banks, Time-Frequency Transforms and Applications. Wiley, New York (1999)"},{"issue":"3","key":"51_CR24","first-page":"1","volume":"38","author":"B \u015een","year":"2014","unstructured":"\u015een, B., Peker, M., \u00c7avu\u015fo\u011flu, A., \u00c7elebi, F.V.: A comparative study on classification of sleep stage based on EEG signals using feature selection and classification algorithms. J. Med. Syst. 38(3), 1\u201321 (2014)","journal-title":"J. Med. Syst."},{"key":"51_CR25","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1016\/j.eswa.2016.07.004","volume":"63","author":"M Diykh","year":"2016","unstructured":"Diykh, M., Li, Y.: Complex networks approach for EEG signal sleep stages classification. Expert Syst. Appl. 63, 241\u2013248 (2016)","journal-title":"Expert Syst. Appl."},{"key":"51_CR26","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1016\/j.ins.2016.09.038","volume":"384","author":"M Bach","year":"2016","unstructured":"Bach, M., Werner, A., \u017bywiec, J., Pluskiewicz, W.: The study of under-and over-sampling methods\u2019 utility in analysis of highly imbalanced data on osteoporosis. Inf. Sci. 384, 174\u2013190 (2016)","journal-title":"Inf. Sci."},{"issue":"2","key":"51_CR27","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1016\/j.tele.2015.08.006","volume":"33","author":"C-H Weng","year":"2016","unstructured":"Weng, C.-H., Huang, T.C.-K., Han, R.-P.: Disease prediction with different types of neural network classifiers. Telemat. Inform. 33(2), 277\u2013292 (2016)","journal-title":"Telemat. Inform."},{"key":"51_CR28","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1016\/j.ins.2014.07.044","volume":"318","author":"J Zhang","year":"2015","unstructured":"Zhang, J., Li, H., Gao, Q., Wang, H., Luo, Y.: Detecting anomalies from big network traffic data using an adaptive detection approach. Inf. Sci. 318, 91\u2013110 (2015). Elsevier Publisher","journal-title":"Inf. Sci."},{"key":"51_CR29","doi-asserted-by":"crossref","unstructured":"Zhang, J., Gao, Q., Wang, H.: SPOT: a system for detecting projected outliers from high-dimensional data streams. In: 24th IEEE International Conference on Data Engineering (ICDE 2008), pp. 1628\u20131631. IEEE Computer Society, Cancun, April 2008","DOI":"10.1109\/ICDE.2008.4497638"}],"container-title":["Lecture Notes in Computer Science","Advances in Knowledge Discovery and Data Mining"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-57454-7_51","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T23:45:49Z","timestamp":1750203949000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-319-57454-7_51"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017]]},"ISBN":["9783319574530","9783319574547"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-57454-7_51","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2017]]},"assertion":[{"value":"23 April 2017","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PAKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pacific-Asia Conference on Knowledge Discovery and Data Mining","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Jeju","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Korea (Republic of)","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2017","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 May 2017","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 May 2017","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pakdd2017","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/pakdd2017.snu.ac.kr\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}