Abrougui K, Gabsi K, Mercatoris B, Khemis C, Amami R, Chehaibi S. Prediction of organic potato yield using tillage systems and soil properties by artificial neural network (ANN) and multiple linear regressions (MLR). Soil Tillage Res. 2019;190:202–8.
Al-Najjar H, Al-Rousan N. A classifier prediction model to predict the status of coronavirus COVID-19 patients in South Korea. Eur Rev Med Pharmacol Sci. 2020;24(6):3400–3.
Amoozad-Khalili M, Rostamian R, Esmaeilpour-Troujeni M, Kosari-Moghaddam A. Economic modeling of mechanized and semi-mechanized rainfed wheat production systems using multiple linear regression model. Inf Process Agric. 2020;7(1):30–40.
Anai M, Akaike K, Iwagoe H, Akasaka T, Higuchi T, Miyazaki A, Naito D, Tajima Y, Takahashi H, Komatsu T, et al. Decrease in hemoglobin level predicts increased risk for severe respiratory failure in COVID-19 patients with pneumonia. Respir Investig. 2021;59(2):187–93.
Bai X, Fang C, Zhou Y, Bai S, Liu Z, Xia L, Chen Q, Xu Y, Xia T, Gong S, et al. Predicting COVID-19 malignant progression with AI techniques. J Clin Med. 2020;9(6):1668.
Bi X, Su Z, Yan H, Du J, Wang J, Chen L, Peng M, Chen S, Shen B, Li J. Prediction of severe illness due to COVID-19 based on an analysis of initial fibrinogen to albumin ratio and platelet count. Platelets. 2020;31(5):674–9.
Çerçi KN, Hürdoğan E. Comparative study of multiple linear regression (MLR) and artificial neural network (ANN) techniques to model a solid desiccant wheel. Int Commun Heat Mass Transf. 2020;116: 104713.
Chen R, Liang W, Jiang M, Guan W, Zhan C, Wang T, Tang C, Sang L, Liu J, Ni Z, et al. Risk factors of fatal outcome in hospitalized subjects with coronavirus disease 2019 from a nationwide analysis in China. Chest. 2020;158(1):97–105.
Cheng FY, Joshi H, Tandon P, Freeman R, Reich DL, Mazumdar M, Kohli-Seth R, Levin MA, Timsina P, Kia A. Using machine learning to predict ICU transfer in hospitalized COVID-19 patients. J Clin Med. 2020;9(6):1668.
Ciulla G, D’Amico A. Building energy performance forecasting: a multiple linear regression approach. Appl Energy. 2019;253:113500.
Cogoljević D, Gavrilović M, Roganović M, Matić I, Piljan I. Analyzing of consumer price index influence on inflation by multiple linear regression. Physica A. 2018;505:941–4.
Dong YM, Sun J, Li YX, Chen Q, Liu QQ, Sun Z, Pang R, Chen F, Xu BY, Manyande A, et al. Development and validation of a nomogram for assessing survival in patients with COVID-19 pneumonia. Clin Infect Dis. 2021;72(4):652–60.
Etemadi S, Khashei M. Etemadi multiple linear regression. Measurement. 2021;186: 110080.
Francone M, Iafrate F, Masci GM, Coco S, Cilia F, Manganaro L, Panebianco V, Andreoli C, Colaiacomo MC, Zingaropoli MA, et al. Chest CT score in COVID-19 patients: correlation with disease severity and short-term prognosis. Eur Radiol. 2020;30(12):6808–17.
Gallo Marin B, Aghagoli G, Lavine K, Yang L, Siff EJ, Chiang SS, Salazar-Mather TP, Dumenco L, Savaria MC, Aung SN, et al. Predictors of COVID-19 severity: a literature review. Rev Med Virol. 2021;31(1):1–10.
Hajiahmadi S, Shayganfar A, Janghorbani M, Esfahani MM, Mahnam M, Bakhtiarvand N, Sami R, Khademi N, Dehghani M. Chest computed tomography severity score to predict adverse outcomes of patients with COVID-19. Infect Chemother. 2021;53(2):308.
Hoang ND. Estimating punching shear capacity of steel fibre reinforced concrete slabs using sequential piecewise multiple linear regression and artificial neural network. Measurement. 2019;137:58–70.
Homayounieh F, Ebrahimian S, Babaei R, Mobin HK, Zhang E, Bizzo BC, Mohseni I, Digumarthy SR, Kalra MK (2020) CT radiomics, radiologists, and clinical information in predicting outcome of patients with COVID-19 pneumonia. Radiol Cardiothorac Imaging 2(4):e200322
Huang H, Cai S, Li Y, Li Y, Fan Y, Li L, Lei C, Tang X, Hu F, Li F, et al. Prognostic factors for COVID-19 pneumonia progression to severe symptoms based on earlier clinical features: a retrospective analysis. Front Med. 2020;7:643.
Huang ZY, Lin S, Long LL, Cao JY, Luo F, Qin WC, Sun DM, Gregersen H. Predicting the morbidity of chronic obstructive pulmonary disease based on multiple locally weighted linear regression model with k-means clustering. Int J Med Inform. 2020;139: 104141.
Kern C, Stefan T, Hinrichs J. Multiple linear regression modeling: prediction of cheese curd dry matter during curd treatment. Food Res Int. 2019;121:471–8.
Khashei M, Bakhtiarvand N, Etemadi S. A novel reliability-based regression model for medical modeling and forecasting. Diabetes Metab Syndr Clin Res Rev. 2021;15(6): 102331.
Khemet B, Richman R. A univariate and multiple linear regression analysis on a national fan (de) pressurization testing database to predict airtightness in houses. Build Environ. 2018;146:88–97.
Kusano M, Miyazaki S, Watanabe M, Kishimoto S, Bulgarevich DS, Ono Y, Yumoto A. Tensile properties prediction by multiple linear regression analysis for selective laser melted and post heat-treated Ti-6Al-4V with microstructural quantification. Mater Sci Eng A. 2020;787:139549.
Lee Y, Jung C, Kim S. Spatial distribution of soil moisture estimates using a multiple linear regression model and Korean geostationary satellite (coms) data. Agric Water Manag. 2019;213:580–93.
Li C, Ye J, Chen Q, Hu W, Wang L, Fan Y, Lu Z, Chen J, Chen Z, Chen S, et al. Elevated lactate dehydrogenase (LDH) level as an independent risk factor for the severity and mortality of COVID-19. Aging (Albany NY). 2020;12(15):15670.
Li D, Zhang Q, Tan Y, Feng X, Yue Y, Bai Y, Li J, Li J, Xu Y, Chen S, et al. Prediction of COVID-19 severity using chest computed tomography and laboratory measurements: evaluation using a machine learning approach. JMIR Med Inform. 2020;8(11): e21604.
Lu C, Liu Y, Chen B, Yang H, Hu H, Zhao Y. Prognostic value of lymphocyte count in severe COVID-19 patients with corticosteroid treatment. Signal Transduct Target Ther. 2021;6(1):1–3.
Matos J, Paparo F, Mussetto I, Bacigalupo L, Veneziano A, Bernardi SP, Biscaldi E, Melani E, Antonucci G, Cremonesi P, et al. Evaluation of novel coronavirus disease (COVID-19) using quantitative lung CT and clinical data: prediction of short-term outcome. Eur Radiol Exp. 2020;4(1):1–10.
McRae MP, Simmons GW, Christodoulides NJ, Lu Z, Kang SK, Fenyo D, Alcorn T, Dapkins IP, Sharif I, Vurmaz D, et al. Clinical decision support tool and rapid point-of-care platform for determining disease severity in patients with covid-19. Lab Chip. 2020;20(12):2075–85.
Ning W, Lei S, Yang J, Cao Y, Jiang P, Yang Q, Zhang J, Wang X, Chen F, Geng Z, et al. Open resource of clinical data from patients with pneumonia for the prediction of COVID-19 outcomes via deep learning. Nat Biomed Eng. 2020;4(12):1197–207.
Pahlavan-Rad MR, Dahmardeh K, Hadizadeh M, Keykha G, Mohammadnia N, Gangali M, Keikha M, Davatgar N, Brungard C. Prediction of soil water infiltration using multiple linear regression and random forest in a dry flood plain, Eastern Iran CATENA. 2020;194:104715.
Park SK, Moon HJ, Min KC, Hwang C, Kim S. Application of a multiple linear regression and an artificial neural network model for the heating performance analysis and hourly prediction of a large-scale ground source heat pump system. Energy Build. 2018;165:206–15.
Rahbari A, Josephson TR, Sun Y, Moultos OA, Dubbeldam D, Siepmann JI, Vlugt TJ. Multiple linear regression and thermodynamic fluctuations are equivalent for computing thermodynamic derivatives from molecular simulation. Fluid Phase Equilib. 2020;523: 112785.
Rath S, Tripathy A, Tripathy AR. Prediction of new active cases of coronavirus disease (COVID-19) pandemic using multiple linear regression model. Diabetes Metab Syndr Clin Res Rev. 2020;14(5):1467–74.
Rokni M, Ahmadikia K, Asghari S, Mashaei S, Hassanali F. Comparison of clinical, para-clinical and laboratory findings in survived and deceased patients with COVID-19: diagnostic role of inflammatory indications in determining the severity of illness. BMC Infect Dis. 2020;20(1):1–11.
Shi W, Peng X, Liu T, Cheng Z, Lu H, Yang S, Zhang J, Wang M, Gao Y, Shi Y, et al. A deep learning-based quantitative computed tomography model for predicting the severity of COVID-19: a retrospective study of 196 patients. Ann Transl Med. 2021;9(3):216–28.
Siavash NK, Ghobadian B, Najafi G, Rohani A, Tavakoli T, Mahmoodi E, Mamat R, et al. Prediction of power generation and rotor angular speed of a small wind turbine equipped to a controllable duct using artificial neural network and multiple linear regression. Environ Res. 2021;196: 110434.
Stoichev T, Coelho JP, De Diego A, Valenzuela MGL, Pereira ME, de Chanvalon AT, Amouroux D. Multiple regression analysis to assess the contamination with metals and metalloids in surface sediments (Aveiro Lagoon, Portugal). Mar Pollut Bull. 2020;159: 111470.
Tang Q, Huang L, Pan Z. Multiple linear regression model for vascular aging assessment based on radial artery pulse wave. Eur J Integr Med. 2019;28:92–7.
Tang W, Li Y, Yu Y, Wang Z, Xu T, Chen J, Lin J, Li X. Development of models predicting biodegradation rate rating with multiple linear regression and support vector machine algorithms. Chemosphere. 2020;253: 126666.
Wei W, Hu XW, Cheng Q, Zhao YM, Ge YQ. Identification of common and severe COVID-19: the value of CT texture analysis and correlation with clinical characteristics. Eur Radiol. 2020;30(12):6788–96.
Wu MY, Yao L, Wang Y, Zhu XY, Wang XF, Tang PJ, Chen C. Clinical evaluation of potential usefulness of serum lactate dehydrogenase (LDH) in 2019 novel coronavirus (COVID-19) pneumonia. Respir Res. 2020;21(1):1–6.
Xiao J, Li X, Xie Y, Huang Z, Ding Y, Zhao S, Yang P, Du D, Liu B, Wang X. Maximum chest CT score is associated with progression to severe illness in patients with COVID-19: a retrospective study from Wuhan. China BMC Infect Dis. 2020;20(1):1–11.
Xie X, Wu T, Zhu M, Jiang G, Xu Y, Wang X, Pu L. Comparison of random forest and multiple linear regression models for estimation of soil extracellular enzyme activities in agricultural reclaimed coastal saline land. Ecol Ind. 2021;120: 106925.
Yan L, Zhang HT, Xiao Y, Wang M, Sun C, Liang J, Li S, Zhang M, Guo Y, Xiao Y, et al. (2020) Prediction of survival for severe COVID-19 patients with three clinical features: development of a machine learning-based prognostic model with clinical data in Wuhan. medRxiv
Yuchi W, Gombojav E, Boldbaatar B, Galsuren J, Enkhmaa S, Beejin B, Naidan G, Ochir C, Legtseg B, Byambaa T, et al. Evaluation of random forest regression and multiple linear regression for predicting indoor fine particulate matter concentrations in a highly polluted city. Environ Pollut. 2019;245:746–53.
Zhang C, Qin L, Li K, Wang Q, Zhao Y, Xu B, Liang L, Dai Y, Feng Y, Sun J, et al. A novel scoring system for prediction of disease severity in COVID-19. Front Cell Infect Microbiol. 2020;10:318.
Zhang S, Guo M, Duan L, Wu F, Hu G, Wang Z, Huang Q, Liao T, Xu J, Ma Y, et al. Development and validation of a risk factor-based system to predict short-term survival in adult hospitalized patients with COVID-19: a multicenter, retrospective, cohort study. Crit Care. 2020;24(1):1–13.
Zheng X, Jiang Z, Ying Z, Song J, Chen W, Wang B. Role of feedstock properties and hydrothermal carbonization conditions on fuel properties of sewage sludge-derived hydrochar using multiple linear regression technique. Fuel. 2020;271: 117609.
Zhou K, Sun Y, Li L, Zang Z, Wang J, Li J, Liang J, Zhang F, Zhang Q, Ge W, et al. Eleven routine clinical features predict COVID-19 severity uncovered by machine learning of longitudinal measurements. Comput Struct Biotechnol J. 2021;19:3640–9.
Zhou S, Chen C, Hu Y, Lv W, Ai T, Xia L. Chest CT imaging features and severity scores as biomarkers for prognostic prediction in patients with COVID-19. Ann Transl Med. 2020;8(21)
Zhou Y, He Y, Yang H, Yu H, Wang T, Chen Z, Yao R, Liang Z. Development and validation a nomogram for predicting the risk of severe COVID-19: a multi-center study in Sichuan, China. PLoS ONE. 2020;15(5): e0233328.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.