{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,26]],"date-time":"2025-12-26T07:09:57Z","timestamp":1766732997965,"version":"3.37.3"},"reference-count":72,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2024,1,10]],"date-time":"2024-01-10T00:00:00Z","timestamp":1704844800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,1,10]],"date-time":"2024-01-10T00:00:00Z","timestamp":1704844800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100006191","name":"Eskisehir Osmangazi University","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100006191","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2024,4]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Data mining methods are important for the diagnosis and prediction of diseases. Early and accurate diagnosis of patients is vital for their treatment. Various methods have been used in the literature to classify anemia. However, due to the different characteristics of patient datasets, changes in dataset sizes, different parameter numbers and features, and different numbers of patient records, algorithm performances vary according to datasets. In this study, the Harris hawks algorithm (HHA) and the multivariate adaptive regression spline (MARS) were used to classify anemia based on blood data of 1732 patients from the Kaggle database of patients with and without anemia. Six different algorithms were proposed to determine the parameters of the linear anemia approximation, namely multilinear form HHA, multilinear quadratic form HHA, multilinear exponential form HHA, first-order MARS model, second-order MARS model, and the best performing MARS model. The performance of the six proposed algorithms has been analyzed and found to be better than the previous studies in the literature.<\/jats:p>","DOI":"10.1007\/s00521-023-09379-y","type":"journal-article","created":{"date-parts":[[2024,1,10]],"date-time":"2024-01-10T20:02:17Z","timestamp":1704916937000},"page":"5653-5672","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Classification of anemia using Harris hawks optimization method and multivariate adaptive regression spline"],"prefix":"10.1007","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6407-4338","authenticated-orcid":false,"given":"Nagihan","family":"Yagmur","sequence":"first","affiliation":[]},{"given":"\u0130diris","family":"Dag","sequence":"additional","affiliation":[]},{"given":"Hasan","family":"Temurtas","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,1,10]]},"reference":[{"key":"9379_CR1","unstructured":"De Benoist B, Cogswell M, Egli I, McLean E (2008) Worldwide prevalence of anaemia 1993\u20132005; WHO Global Database of anaemia"},{"key":"9379_CR2","unstructured":"WHO. \u201cAnaemia,\u201d World Health Organization. https:\/\/www.who.int\/healthtopics\/anaemia#tab=tab_1. Accessed 04 Oct 2023"},{"issue":"1","key":"9379_CR3","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.medengphy.2013.05.013","volume":"36","author":"L Moraru","year":"2014","unstructured":"Moraru L, Moldovanu S, Biswas A (2014) Optimization of breast lesion segmentation in texture feature space approach. Med Eng Phys 36(1):129\u2013135","journal-title":"Med Eng Phys"},{"issue":"3","key":"9379_CR4","doi-asserted-by":"crossref","first-page":"843","DOI":"10.1016\/j.bbe.2019.07.005","volume":"39","author":"N Dey","year":"2019","unstructured":"Dey N et al (2019) Social-group-optimization based tumor evaluation tool for clinical brain MRI of Flair\/diffusion-weighted modality. Biocybern Biomed Eng 39(3):843\u2013856","journal-title":"Biocybern Biomed Eng"},{"key":"9379_CR5","doi-asserted-by":"crossref","first-page":"1578","DOI":"10.1016\/j.procs.2018.05.122","volume":"132","author":"D Sisodia","year":"2018","unstructured":"Sisodia D, Sisodia DS (2018) Prediction of diabetes using classification algorithms. Procedia Comput Sci 132:1578\u20131585","journal-title":"Procedia Comput Sci"},{"key":"9379_CR6","doi-asserted-by":"crossref","unstructured":"Thirunavukkarasu K, Singh AS, Rai P, Gupta S (2018) Classification of IRIS dataset using classification based KNN algorithm in supervised learning. \u0130n: 2018 4th \u0131nternational conference on computing communication and automation (ICCCA), IEEE, 2018, pp 1\u20134","DOI":"10.1109\/CCAA.2018.8777643"},{"issue":"2","key":"9379_CR7","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.artmed.2014.08.003","volume":"62","author":"K Kuru","year":"2014","unstructured":"Kuru K, Niranjan M, Tunca Y, Osvank E, Azim T (2014) Biomedical visual data analysis to build an intelligent diagnostic decision support system in medical genetics. Artif Intell Med 62(2):105\u2013118","journal-title":"Artif Intell Med"},{"key":"9379_CR8","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1016\/j.dss.2014.01.005","volume":"59","author":"A Bourouis","year":"2014","unstructured":"Bourouis A, Feham M, Hossain MA, Zhang L (2014) An intelligent mobile based decision support system for retinal disease diagnosis. Decis Supp Syst 59:341\u2013350","journal-title":"Decis Supp Syst"},{"issue":"6","key":"9379_CR9","doi-asserted-by":"crossref","first-page":"775","DOI":"10.1002\/jemt.23222","volume":"82","author":"T Saba","year":"2019","unstructured":"Saba T et al (2019) Cloud-based decision support system for the detection and classification of malignant cells in breast cancer using breast cytology images. Microsc Res Tech 82(6):775\u2013785","journal-title":"Microsc Res Tech"},{"key":"9379_CR10","doi-asserted-by":"crossref","DOI":"10.1515\/9783110621105","volume-title":"Intelligent decision support systems: applications in signal processing","author":"S Borra","year":"2019","unstructured":"Borra S, Dey N, Bhattacharyya S, Bouhlel MS (2019) Intelligent decision support systems: applications in signal processing, vol 4. Walter de Gruyter GmbH & Co KG, Berlin"},{"key":"9379_CR11","doi-asserted-by":"crossref","first-page":"104164","DOI":"10.1016\/j.bspc.2022.104164","volume":"79","author":"P\u00d6 Kavas","year":"2023","unstructured":"Kavas P\u00d6, Bozkurt MR, Kocayi\u011fit \u0130, Bilgin C (2023) Machine learning-based medical decision support system for diagnosing HFpEF and HFrEF using PPG. Biomed Signal Process Control 79:104164","journal-title":"Biomed Signal Process Control"},{"issue":"1","key":"9379_CR12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12911-019-1004-8","volume":"19","author":"S Uddin","year":"2019","unstructured":"Uddin S, Khan A, Hossain ME, Moni MA (2019) Comparing different supervised machine learning algorithms for disease prediction. BMC Med Inform Decis Mak 19(1):1\u201316","journal-title":"BMC Med Inform Decis Mak"},{"key":"9379_CR13","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2020.102231","volume":"63","author":"S Kilicarslan","year":"2021","unstructured":"Kilicarslan S, Celik M, Sahin \u015e (2021) Hybrid models based on genetic algorithm and deep learning algorithms for nutritional Anemia disease classification. Biomed Signal Process Control 63:102231","journal-title":"Biomed Signal Process Control"},{"key":"9379_CR14","unstructured":"Yagmur N, Alagoz BB Modeling of first order plus time delay system dynamics with adaptive IIR filters based on gradient descent methods and performance analyses for different time delay cases. Pamukkale Univ J Eng Sci, vol 1000, no 1000"},{"issue":"7","key":"9379_CR15","first-page":"2121","volume":"12","author":"J Duchi","year":"2011","unstructured":"Duchi J, Hazan E, Singer Y (2011) Adaptive subgradient methods for online learning and stochastic optimization. J Mach Learn Res 12(7):2121\u20132159","journal-title":"J Mach Learn Res"},{"issue":"1\u20132","key":"9379_CR16","first-page":"118","volume":"10","author":"G Manogaran","year":"2018","unstructured":"Manogaran G, Lopez D (2018) Health data analytics using scalable logistic regression with stochastic gradient descent. Int J Adv Intell Paradig 10(1\u20132):118\u2013132","journal-title":"Int J Adv Intell Paradig"},{"issue":"4","key":"9379_CR17","first-page":"91","volume":"9","author":"M Dixit","year":"2015","unstructured":"Dixit M, Upadhyay N, Silakari S (2015) An exhaustive survey on nature inspired optimization algorithms. Int J Softw Eng Its Appl 9(4):91\u2013104","journal-title":"Int J Softw Eng Its Appl"},{"key":"9379_CR18","unstructured":"Kumar SR, Singh KD (2021) Nature-inspired optimization algorithms: research direction and survey. arXiv preprint arXiv:2102.04013"},{"key":"9379_CR19","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/8512469","author":"N Gundluru","year":"2022","unstructured":"Gundluru N et al (2022) Enhancement of detection of diabetic retinopathy using Harris hawks optimization with deep learning model. Comput Intell Neurosci. https:\/\/doi.org\/10.1155\/2022\/8512469","journal-title":"Comput Intell Neurosci"},{"key":"9379_CR20","doi-asserted-by":"crossref","first-page":"877","DOI":"10.1007\/s00521-017-3047-z","volume":"31","author":"A Kumar","year":"2019","unstructured":"Kumar A, Kabra G, Mussada EK, Dash MK, Rana PS (2019) Combined artificial bee colony algorithm and machine learning techniques for prediction of online consumer repurchase intention. Neural Comput Appl 31:877\u2013890","journal-title":"Neural Comput Appl"},{"issue":"1","key":"9379_CR21","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.eswa.2003.12.013","volume":"27","author":"S-M Chou","year":"2004","unstructured":"Chou S-M, Lee T-S, Shao YE, Chen I-F (2004) Mining the breast cancer pattern using artificial neural networks and multivariate adaptive regression splines. Expert Syst Appl 27(1):133\u2013142","journal-title":"Expert Syst Appl"},{"key":"9379_CR22","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1016\/j.jhydrol.2019.05.046","volume":"575","author":"DT Bui","year":"2019","unstructured":"Bui DT et al (2019) A new intelligence approach based on GIS-based multivariate adaptive regression splines and metaheuristic optimization for predicting flash flood susceptible areas at high-frequency tropical typhoon area. J Hydrol 575:314\u2013326","journal-title":"J Hydrol"},{"key":"9379_CR23","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1016\/j.tust.2017.07.013","volume":"70","author":"ATC Goh","year":"2017","unstructured":"Goh ATC, Zhang Y, Zhang R, Zhang W, Xiao Y (2017) Evaluating stability of underground entry-type excavations using multivariate adaptive regression splines and logistic regression. Tunn Undergr Sp Technol 70:148\u2013154","journal-title":"Tunn Undergr Sp Technol"},{"key":"9379_CR24","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.atmosres.2016.10.004","volume":"184","author":"RC Deo","year":"2017","unstructured":"Deo RC, Kisi O, Singh VP (2017) Drought forecasting in eastern Australia using multivariate adaptive regression spline, least square support vector machine and M5Tree model. Atmos Res 184:149\u2013175","journal-title":"Atmos Res"},{"key":"9379_CR25","doi-asserted-by":"publisher","DOI":"10.1029\/2005WR004528","author":"MK Gill","year":"2006","unstructured":"Gill MK, Kaheil YH, Khalil A, McKee M, Bastidas L (2006) Multiobjective particle swarm optimization for parameter estimation in hydrology. Water Resour Res. https:\/\/doi.org\/10.1029\/2005WR004528","journal-title":"Water Resour Res"},{"key":"9379_CR26","doi-asserted-by":"crossref","unstructured":"K\u00fc\u00e7\u00fckk\u00fclahli E, Erdo\u011fmu\u015f P, Polat K (2017) A hybrid approach to image segmentation: combination of BBO (Biogeography based optimization) and histogram based cluster estimation. \u0130n: 2017 25th signal processing and communications applications conference (SIU), IEEE, 2017, pp 1\u20134","DOI":"10.1109\/SIU.2017.7960188"},{"key":"9379_CR27","unstructured":"Ya\u011fmur N (2023) \u00dc\u00e7 Boyutlu Engelli K\u00fcbik Bir Ortamda Genetik Algoritma \u0130le Robot Yol Planlamas\u0131nda En K\u0131sa Yol Bulma"},{"key":"9379_CR28","doi-asserted-by":"crossref","unstructured":"Ya\u011fmur N, Alag\u00f6z BB (2019) Comparision of solutions of numerical gradient descent method and continous time gradient descent dynamics and lyapunov stability. \u0130n: 2019 27th signal processing and communications applications conference (SIU), IEEE, pp 1\u20134","DOI":"10.1109\/SIU.2019.8806396"},{"issue":"4","key":"9379_CR29","doi-asserted-by":"crossref","first-page":"1251","DOI":"10.55730\/1300-0632.3847","volume":"30","author":"D \u00d6zdemir","year":"2022","unstructured":"\u00d6zdemir D, D\u00f6rterler S (2022) An adaptive search equation-based artificial bee colony algorithm for transportation energy demand forecasting. Turkish J Electr Eng Comput Sci 30(4):1251\u20131268","journal-title":"Turkish J Electr Eng Comput Sci"},{"issue":"2","key":"9379_CR30","doi-asserted-by":"crossref","first-page":"214","DOI":"10.11121\/ijocta.2023.1269","volume":"13","author":"A Ahmad","year":"2023","unstructured":"Ahmad A, Alzaidi K, Sari M, Uslu H (2023) Prediction of anemia with a particle swarm optimization-based approach. Int J. Optim. Control Theor. Appl. 13(2):214\u2013223","journal-title":"Int J. Optim. Control Theor. Appl."},{"key":"9379_CR31","volume":"138","author":"B \u00c7il","year":"2020","unstructured":"\u00c7il B, Ayy\u0131ld\u0131z H, Tuncer T (2020) Discrimination of \u03b2-thalassemia and iron deficiency anemia through extreme learning machine and regularized extreme learning machine based decision support system. Med Hypotheses 138:109611","journal-title":"Med Hypotheses"},{"issue":"3","key":"9379_CR32","doi-asserted-by":"crossref","first-page":"771","DOI":"10.1016\/j.ins.2006.07.009","volume":"177","author":"W Wongseree","year":"2007","unstructured":"Wongseree W, Chaiyaratana N, Vichittumaros K, Winichagoon P, Fucharoen S (2007) Thalassaemia classification by neural networks and genetic programming. Inf Sci (NY) 177(3):771\u2013786","journal-title":"Inf Sci (NY)"},{"issue":"1","key":"9379_CR33","first-page":"85","volume":"3","author":"S Dogan","year":"2008","unstructured":"Dogan S, Turkoglu I (2008) Iron-deficiency anemia detection from hematology parameters by using decision trees. Int J Sci Technol 3(1):85\u201392","journal-title":"Int J Sci Technol"},{"key":"9379_CR34","doi-asserted-by":"crossref","unstructured":"Sanap SA, Nagori M, Kshirsagar V (2011) Classification of anemia using data mining techniques. \u0130n: International conference on swarm, evolutionary, and memetic computing, Springer, Berlin, pp 113\u2013121","DOI":"10.1007\/978-3-642-27242-4_14"},{"issue":"6","key":"9379_CR35","doi-asserted-by":"crossref","first-page":"7415","DOI":"10.1016\/j.eswa.2010.12.083","volume":"38","author":"N Allahverdi","year":"2011","unstructured":"Allahverdi N, Tunali A, I\u015fik H, Kahramanli H (2011) A Takagi-Sugeno type neuro-fuzzy network for determining child anemia. Expert Syst Appl 38(6):7415\u20137418","journal-title":"Expert Syst Appl"},{"key":"9379_CR36","doi-asserted-by":"crossref","first-page":"2057","DOI":"10.1007\/s10916-011-9668-3","volume":"36","author":"I Azarkhish","year":"2012","unstructured":"Azarkhish I, Raoufy MR, Gharibzadeh S (2012) Artificial intelligence models for predicting iron deficiency anemia and iron serum level based on accessible laboratory data. J Med Syst 36:2057\u20132061","journal-title":"J Med Syst"},{"key":"9379_CR37","doi-asserted-by":"crossref","first-page":"2941","DOI":"10.1007\/s10916-011-9772-4","volume":"36","author":"Z Y\u0131lmaz","year":"2012","unstructured":"Y\u0131lmaz Z, Bozkurt MR (2012) Determination of women iron deficiency anemia using neural networks. J Med Syst 36:2941\u20132945","journal-title":"J Med Syst"},{"issue":"2","key":"9379_CR38","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1016\/j.bspc.2011.03.007","volume":"7","author":"D Setsirichok","year":"2012","unstructured":"Setsirichok D et al (2012) Classification of complete blood count and haemoglobin typing data by a C4. 5 decision tree, a na\u00efve Bayes classifier and a multilayer perceptron for thalassaemia screening. Biomed Signal Process Control 7(2):202\u2013212","journal-title":"Biomed Signal Process Control"},{"key":"9379_CR39","unstructured":"Akrimi JA, Rahimahmad A, George LE (2013) Review of machine learning techniques in Anemia recognition. Int J Sci Res (IJSR), India Online ISSN, pp 2319\u20137064"},{"key":"9379_CR40","unstructured":"Abdullah M, Al-Asmari S (2017) Anemia types prediction based on data mining classification algorithms. In: Communication, management and \u0131nformation technology, Alencar pp 615\u2013621"},{"key":"9379_CR41","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.cmpb.2017.11.015","volume":"168","author":"AI Shahin","year":"2019","unstructured":"Shahin AI, Guo Y, Amin KM, Sharawi AA (2019) White blood cells identification system based on convolutional deep neural learning networks. Comput Methods Programs Biomed 168:69\u201380","journal-title":"Comput Methods Programs Biomed"},{"key":"9379_CR42","doi-asserted-by":"crossref","first-page":"46968","DOI":"10.1109\/ACCESS.2018.2867110","volume":"6","author":"G Dimauro","year":"2018","unstructured":"Dimauro G, Caivano D, Girardi F (2018) A new method and a non-invasive device to estimate anemia based on digital images of the conjunctiva. IEEE Access 6:46968\u201346975","journal-title":"IEEE Access"},{"key":"9379_CR43","doi-asserted-by":"crossref","unstructured":"\u0130laslaner T, G\u00fcven A (2019) Investigation of the effects biochemistry on \u0131ron deficiency anemia. \u0130n: 2019 medical technologies congress (TIPTEKNO), IEEE, 2019, pp 1\u20134","DOI":"10.1109\/TIPTEKNO.2019.8895227"},{"issue":"1","key":"9379_CR44","doi-asserted-by":"crossref","first-page":"195","DOI":"10.6339\/JDS.201901_17(1).0009","volume":"17","author":"JR Khan","year":"2019","unstructured":"Khan JR, Chowdhury S, Islam H, Raheem E (2019) Machine learning algorithms to predict the childhood anemia in Bangladesh. J Data Sci 17(1):195\u2013218","journal-title":"J Data Sci"},{"issue":"2","key":"9379_CR45","first-page":"100","volume":"17","author":"EMT El-Kenawy","year":"2019","unstructured":"El-Kenawy EMT (2019) A machine learning model for hemoglobin estimation and anemia classification. Int J Comput Sci Inf Secur 17(2):100\u2013108","journal-title":"Int J Comput Sci Inf Secur"},{"issue":"1","key":"9379_CR46","first-page":"50","volume":"24","author":"TK Y\u0131ld\u0131z","year":"2021","unstructured":"Y\u0131ld\u0131z TK, Yurtay N, \u00d6ne\u00e7 B (2021) Classifying anemia types using artificial learning methods. Eng Sci Technol Int J 24(1):50\u201370","journal-title":"Eng Sci Technol Int J"},{"key":"9379_CR47","doi-asserted-by":"crossref","unstructured":"Vohra R, Dudyala AK, Pahareeya J, Hussain A (2022) Decision rules generation using decision tree classifier and their optimization for anemia classification. In: \u0131nventive computation and \u0131nformation technologies: proceedings of ICICIT 2021, Springer, Berlin, pp 721\u2013737","DOI":"10.1007\/978-981-16-6723-7_53"},{"key":"9379_CR48","doi-asserted-by":"crossref","first-page":"849","DOI":"10.1016\/j.future.2019.02.028","volume":"97","author":"AA Heidari","year":"2019","unstructured":"Heidari AA, Mirjalili S, Faris H, Aljarah I, Mafarja M, Chen H (2019) Harris hawks optimization: algorithm and applications. Futur Gener Comput Syst 97:849\u2013872","journal-title":"Futur Gener Comput Syst"},{"key":"9379_CR49","doi-asserted-by":"crossref","first-page":"4047","DOI":"10.1016\/j.egyr.2021.06.085","volume":"7","author":"M Naeijian","year":"2021","unstructured":"Naeijian M, Rahimnejad A, Ebrahimi SM, Pourmousa N, Gadsden SA (2021) Parameter estimation of PV solar cells and modules using Whippy Harris hawks optimization algorithm. Energy Rep 7:4047\u20134063","journal-title":"Energy Rep"},{"issue":"65","key":"9379_CR50","doi-asserted-by":"crossref","first-page":"481","DOI":"10.21205\/deufmd.2020226516","volume":"22","author":"O Akda\u011f","year":"2020","unstructured":"Akda\u011f O, Abdullah A, Yeroglu C (2020) Harris \u015eahini optimizasyon Algoritmas\u0131 ile Aktif G\u00fc\u00e7 Kay\u0131plar\u0131n\u0131n minimizasyonu. Dokuz Eyl\u00fcl \u00dcniversitesi M\u00fchendislik Fak\u00fcltesi Fen ve M\u00fchendislik Derg 22(65):481\u2013490","journal-title":"Dokuz Eyl\u00fcl \u00dcniversitesi M\u00fchendislik Fak\u00fcltesi Fen ve M\u00fchendislik Derg"},{"issue":"9","key":"9379_CR51","doi-asserted-by":"crossref","first-page":"1551","DOI":"10.3390\/pr9091551","volume":"9","author":"S Wang","year":"2021","unstructured":"Wang S, Jia H, Abualigah L, Liu Q, Zheng R (2021) An improved hybrid aquila optimizer and harris hawks algorithm for solving industrial engineering optimization problems. Processes 9(9):1551","journal-title":"Processes"},{"key":"9379_CR52","doi-asserted-by":"crossref","first-page":"1409","DOI":"10.1007\/s00366-019-00892-0","volume":"37","author":"A Abbasi","year":"2021","unstructured":"Abbasi A, Firouzi B, Sendur P (2021) On the application of Harris hawks optimization (HHO) algorithm to the design of microchannel heat sinks. Eng Comput 37:1409\u20131428","journal-title":"Eng Comput"},{"key":"9379_CR53","volume":"142","author":"J Hu","year":"2022","unstructured":"Hu J et al (2022) Detection of COVID-19 severity using blood gas analysis parameters and Harris hawks optimized extreme learning machine. Comput Biol Med 142:105166","journal-title":"Comput Biol Med"},{"key":"9379_CR54","doi-asserted-by":"crossref","first-page":"17787","DOI":"10.1109\/ACCESS.2021.3052835","volume":"9","author":"H Ye","year":"2021","unstructured":"Ye H et al (2021) Diagnosing coronavirus disease 2019 (COVID-19): Efficient Harris hawks-inspired fuzzy K-nearest neighbor prediction methods. IEEE Access 9:17787\u201317802","journal-title":"IEEE Access"},{"key":"9379_CR55","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11063-021-10700-w","volume":"55","author":"F Jiang","year":"2022","unstructured":"Jiang F, Zhu Q, Tian T (2022) Breast cancer detection based on modified Harris hawks optimization and extreme learning machine embedded with feature weighting. Neural Process Lett 55:1\u201324. https:\/\/doi.org\/10.1007\/s11063-021-10700-w","journal-title":"Neural Process Lett"},{"issue":"5","key":"9379_CR56","first-page":"399","volume":"3","author":"T \u015eenol\u00c7elik","year":"2018","unstructured":"\u015eenol\u00c7elik T, Yusuf\u015eeng\u00fcl A, Hakan\u0130nci (2018) Invest\u0131gat\u0131on of plant and an\u0131mal product\u0131on values affect\u0131ng consumer pr\u0131ce \u0131ndex by mult\u0131var\u0131ate adapt\u0131ve regress\u0131on. Spl\u0131ne: Turkey Case J 3(5):399\u2013408","journal-title":"Spl\u0131ne: Turkey Case J"},{"key":"9379_CR57","unstructured":"Toprak S (2011) Time series modelling using multivariate adaptive regression splines and conic quadratic programming. Dicle \u00dcniversitesi"},{"key":"9379_CR58","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1007\/s10706-015-9938-9","volume":"34","author":"W Zhang","year":"2016","unstructured":"Zhang W, Goh ATC, Zhang Y (2016) Multivariate adaptive regression splines application for multivariate geotechnical problems with big data. Geotech Geol Eng 34:193\u2013204","journal-title":"Geotech Geol Eng"},{"key":"9379_CR59","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jhydrol.2019.03.004","volume":"573","author":"ZA Al-Sudani","year":"2019","unstructured":"Al-Sudani ZA, Salih SQ, Yaseen ZM (2019) Development of multivariate adaptive regression spline integrated with differential evolution model for streamflow simulation. J Hydrol 573:1\u201312","journal-title":"J Hydrol"},{"issue":"4","key":"9379_CR60","doi-asserted-by":"crossref","first-page":"1447","DOI":"10.17582\/journal.pjz\/2019.51.4.1447.1456","volume":"51","author":"S Celik","year":"2019","unstructured":"Celik S (2019) Comparing predictive performances of tree-based data mining algorithms and MARS algorithm in the prediction of live body weight from body traits in Pakistan goats. Pak J Zool 51(4):1447\u20131456","journal-title":"Pak J Zool"},{"key":"9379_CR61","unstructured":"\u00d6zfalc\u0131 Y (2008) Multivariate adaptive regression splines: MARS. Gazi \u00dcniversitesi, Ankara"},{"key":"9379_CR62","unstructured":"Kuter S (2014) Atmospheric correction and image classification on MODIS images by nonparametric regression splines"},{"key":"9379_CR63","unstructured":"Di W (2006) Long term fixed mortgage rate prediction using multivariate adaptive regression splines. School of Computer Engineering Nanyang Technological University"},{"key":"9379_CR64","unstructured":"Yerlikaya F (2008) A new contribution to nonlinear robust regression and classification with MARS and its applications to data mining for quality control in manufacturing. Middle East Technical University"},{"issue":"4","key":"9379_CR65","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1093\/clinchem\/39.4.561","volume":"39","author":"MH Zweig","year":"1993","unstructured":"Zweig MH, Campbell G (1993) Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clin Chem 39(4):561\u2013577","journal-title":"Clin Chem"},{"key":"9379_CR66","doi-asserted-by":"crossref","unstructured":"Smith BJ, Hillis SL (2022) MATLAB toolbox for ROC analysis of multi-reader multi-case diagnostic imaging studies. \u0130n: Medical \u0131maging 2022: \u0131mage perception, observer performance, and technology assessment, SPIE, 2022, pp 99\u2013111","DOI":"10.1117\/12.2610663"},{"key":"9379_CR67","doi-asserted-by":"crossref","unstructured":"Gu Q, Cai Z, Zhu L, Huang B (2008) Data mining on imbalanced data sets. \u0130n: 2008 \u0131nternational conference on advanced computer theory and engineering, IEEE, pp 1020\u20131024","DOI":"10.1109\/ICACTE.2008.26"},{"key":"9379_CR68","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2020.114035","volume":"164","author":"B Mirzaei","year":"2021","unstructured":"Mirzaei B, Nikpour B, Nezamabadi-pour H (2021) CDBH: A clustering and density-based hybrid approach for imbalanced data classification. Expert Syst Appl 164:114035","journal-title":"Expert Syst Appl"},{"key":"9379_CR69","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.ins.2018.04.068","volume":"454","author":"GY Wong","year":"2018","unstructured":"Wong GY, Leung FHF, Ling S-H (2018) A hybrid evolutionary preprocessing method for imbalanced datasets. Inf Sci (NY) 454:161\u2013177","journal-title":"Inf Sci (NY)"},{"issue":"9","key":"9379_CR70","doi-asserted-by":"crossref","first-page":"2850","DOI":"10.1109\/TCYB.2016.2579658","volume":"47","author":"P Lim","year":"2016","unstructured":"Lim P, Goh CK, Tan KC (2016) Evolutionary cluster-based synthetic oversampling ensemble (eco-ensemble) for imbalance learning. IEEE Trans Cybern 47(9):2850\u20132861","journal-title":"IEEE Trans Cybern"},{"issue":"16","key":"9379_CR71","first-page":"7","volume":"5","author":"B\u00c7 Yavuz","year":"2014","unstructured":"Yavuz B\u00c7, Yildiz TK, Yurtay N, Pamuk Z (2014) Comparison of k nearest neighbours and regression tree classifiers used with clonal selection algorithm to diagnose haematological diseases. AJIT-e Acad J Inf Technol 5(16):7\u201320","journal-title":"AJIT-e Acad J Inf Technol"},{"key":"9379_CR72","doi-asserted-by":"crossref","unstructured":"Jaiswal M, Srivastava A, Siddiqui TJ (2019) Machine learning algorithms for anemia disease prediction. \u0130n: Recent trends in communication, computing, and electronics: select proceedings of IC3E 2018, Springer, Berlin, pp 463\u2013469","DOI":"10.1007\/978-981-13-2685-1_44"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-09379-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-023-09379-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-09379-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,7]],"date-time":"2024-11-07T21:25:24Z","timestamp":1731014724000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-023-09379-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,10]]},"references-count":72,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2024,4]]}},"alternative-id":["9379"],"URL":"https:\/\/doi.org\/10.1007\/s00521-023-09379-y","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"type":"print","value":"0941-0643"},{"type":"electronic","value":"1433-3058"}],"subject":[],"published":{"date-parts":[[2024,1,10]]},"assertion":[{"value":"21 April 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 December 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 January 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no relevant financial or nonfinancial interests to disclose.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}