Medical model estimation with particle swarm optimization

Year 2021, Volume: 70 Issue: 1, 468 - 482, 30.06.2021

Murat Sarı , Arshed Ahmad , Hande Uslu

https://doi.org/10.31801/cfsuasmas.644071

Cited By: 2

Abstract

In this paper, a nonlinear medical model based on observational variables has been produced and the particle swarm optimization (PSO) technique, which is an effective technique to predict optimum parameters of the biomedical model, has been used. This study has been conducted on a dataset consisting of 539 subjects. For comparison purposes, nonlinear regression analysis, nonlinear deep learning, and nonlinear regression neural network methods are also considered and the PSO results appear to be slightly better than that of other methods. Built on observational variables and findings, the model is expected to be a good guide for healthcare professionals in diagnosing pathologies and planning treatment programs for their patients. It is therefore strongly believed that the article will be particularly useful for those interested in emerging biomedical models in various medical modelling areas such as infectious and hematological diseases such as anemia.

Keywords

Anemia, medical modelling, nonlinear model, particle swarm optimization

References

• Gulbandilar, E., Cimbiz, A., Sari, M., Ozden, H., Relationship between skin resistance level and static balance in type II diabetic subjects, Diabetes Research and Clinical Practice, 82(3) (2008), 335-339.https://doi.org/10.1016/j.diabres.2008.09.011
• Ahmad, A.A., Sari, M., Anemia prediction with multiple regression support in system medicinal internet of things, Journal of Medical Imaging and Health Informatics, 10(1) (2020) 261-267. https://doi.org/10.1166/jmihi.2020.2839
• Sari, M., Tuna, C., Prediction of pathological subjects using genetic algorithms, Computational and Mathematical Methods in Medicine, 2018, (2018). https://doi.org/10.1155/ 2018/6154025
• Sari, M., Cetiner, B.G., Predicting effect of physical factors on tibial motion using artificial neural networks, Expert Systems with Applications, 36(6) (2009), 9743-9746. https://doi. org/10.1016/j.eswa.2009.02.030
• Cetiner, B.G., Sari, M., Tibial rotation assessment using artificial neural networks, Mathematical and Computational Applications, 15(1) (2010), 34-44. https://doi.org/10.3390/ mca15010034
• Botesteanu, D.A., Lipkowitz, S., Lee, J.M., Levy, D., Mathematical models of breast and ovarian cancers, Wiley Interdisciplinary Reviews: Systems Biology and Medicine, 8(4) (2016), 337-362. https://doi.org/10.1002/wsbm.1343
• Gilli, Q., Mustapha, K., Frayret, J.M., Lahrichi, N., Karimi, E., Patient model for colon and colorectal cancer care trajectory simulation, Health Science Journal, 11(6) (2017), 1-16. https://doi.org/10.21767/1791-809X.1000536
• Loke, Y.H., Harahsheh, A.S., Krieger, A., Olivieri, L.J., Usage of 3D models of tetralogy of Fallot for medical education: impact on learning congenital heart disease, BMC Medical Education, 17(1) (2017), 54. https://doi.org/10.1186/s12909-017-0889-0
• Soler, M.J., Riera, M., Batlle, D., New experimental models of diabetic nephropathy in mice models of type 2 diabetes: e¤orts to replicate human nephropathy, Experimental Diabetes Research, 2012, (2012). https://doi.org/10.1155/2012/616313
• Stephens, M.H., Grey, A., Fernandez, J., Kalluru, R., Faasse, K., Horne, A., Petrie, K.J., 3-D bone models to improve treatment initiation among patients with osteoporosis: A randomised controlled pilot trial,Psychology & Health, 31(4) (2016), 487-497. https://doi.org/10.1080/08870446.2015.1112389
• Rivera, S., Ganz, T., Animal models of anemia of inflammation, In Seminars in Hematology, 46(4) (2009), 351-357. https://doi.org/10.1053/j.seminhematol.2009.06.003
• Weiss, G., Goodnough, L.T., Anemia of chronic disease, New England Journal of Medicine, 352(10) (2005), 1011-1023. https://doi.org/10.1056/NEJMra041809
• Weiss, G., Gasche, C., Pathogenesis and treatment of anemia in inflammatory bowel disease, Haematologica, 95(2) (2010), 175-178. https://doi.org/10.3324/haematol.2009.017046
• Li, X., Dao, M., Lykotrafitis, G., Karniadakis, G.E., Biomechanics and biorheology of red blood cells in sickle cell anemia, Journal of Biomechanics, 50 (2017), 34-41. https://doi. org/10.1016/j.jbiomech.2016.11.022
• Kim, A., Rivera, S., Shprung, D., Limbrick, D., Gabayan, V., Nemeth, E., Ganz, T., Mouse models of anemia of cancer, PLoS One, 9(3) (2014), e93283. https://doi.org/10.1371/ journal.pone.0093283
• Berzuini, C., Franzone, P.C., Stefanelli, M., Viganotti, C., Iron kinetics: modelling and parameter estimation in normal and anemic states, Computers and Biomedical Research, 11(3) (1978), 209-227. https://doi.org/10.1016/0010-4809(78)90008-3
• Barosi, G., Cazzola, M., Morandi, S., Stefanelli, M., Perugini, S., Estimation of ferrokinetic parameters by a mathematical model in patients with primary acquired sideroblastic anaemia, British Journal of Haematology, 39(3) (1978), 409-423. https://doi.org/10.1111/j.1365-2141.1978.tb01112.x
• Dey, S., Raheem, E., Multilevel multinomial logistic regression model for identifying factors associated with anemia in children 6–59 months in northeastern states of India, Cogent Mathematics, 3(1) (2016), 1159798. https://doi.org/10.1080/23311835.2016.1159798
• Mehrara, E., Forssell-Aronsson, E., Johanson, V., Kölby, L., Hultborn, R., Bernhardt, P., A new method to estimate parameters of the growth model for metastatic tumours, Theoretical Biology and Medical Modelling, 10(31) (2013), 1-12. https://doi.org/10.1186/1742-4682-10-31
• Eberhart, R., Kennedy, J., A new optimizer using particle swarm theory, In MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science, IEEE, (1995), 39-43. https://doi.org/10.1109/MHS.1995.494215
• Ozsoy, V.S., Orkcu, H.H., Estimating the parameters of nonlinear regression models through particle swarm optimization, Gazi University Journal of Science, 29(1) (2016), 187-199.
• Abdullah, A., Deris, S., Mohamad, M.S., Anwar, S., An improved swarm optimization for parameter estimation and biological model selection, PLoS One, 8(4) (2013), e61258. https://doi.org/10.1371/journal.pone.0061258
• Chen, S., Yang, R., Yang, R., Yang, L., Yang, X., Xu, C., Xu, B., Zhang, H., Lu, Y., Liu, W., A parameter estimation method for nonlinear systems based on improved boundary chicken swarm optimization, Discrete Dynamics in Nature and Society, 2016, (2016). https://doi.org/10.1155/2016/3795961
• Hong, X., Ding, Y., Ren, L., Chen, L., Huang, B., A weighted heteroscedastic Gaussian Process Modelling via particle swarm optimization, Chemometrics and Intelligent Laboratory Systems, 172 (2017), 129-38. https://doi.org/10.1016/j.chemolab.2017.11.019
• Chang, W.D., Cheng, J.P., Hsu, M.C., Tsai, L.C., Parameter identification of nonlinear systems using a particle swarm optimization approach, In 2012 Third International Conference on Networking and Computing,, IEEE, (2012), 113-117. https://doi.org/10.1109/ICNC.2012.24
• Chu, H.J., Chang, L.C., Applying particle swarm optimization to parameter estimation of the nonlinear Muskingum model, Journal of Hydrologic Engineering, 14(9) (2009), 1024-1027. https://doi.org/10.1061/(ASCE)HE.1943-5584.0000070
• Hosseini, M., Naeini, S.A., Dehghani, A.A., Khaledian, Y., Estimation of soil mechanical resistance parameter by using particle swarm optimization, genetic algorithm and multiple regression methods, Soil and Tillage Research, 157 (2016), 32-42. https://doi.org/10.1016/j.still.2015.11.004
• Jau, Y.M., Su, K.L., Wu, C.J., Jeng, J.T., Modi…ed quantum-behaved particle swarm optimization for parameters estimation of generalized nonlinear multi-regressions model based on Choquet integral with outliers, Applied Mathematics and Computation, 221 (2013), 282-295. https://doi.org/10.1016/j.amc.2013.06.050
• Jahandideh, H., Namvar, M., Use of PSO in Parameter Estimation of Robot Dynamics; Part One: No Need for Parameterization, In System Theory, Control and Computing, ICSTCC, 2012, 16th International Conference, IEEE, (2012), 1-6.
• Erdogmus, P., Ekiz, S., Nonlinear Regression using Particle Swarm Optimization and Genetic Algorithm, International Journal of Computer Applications, 153(6) (2016). https://doi.org/10.5120/ijca2016912081
• Alfiyatin, A.N., Febrita, R.E., Taufiq, H., Mahmudy, W.F., Modeling house price prediction using regression analysis and particle swarm optimization, International Journal of Ad- vanced Computer Science and Applications, 8 (2017). https://doi.org/10.14569/IJACSA.2017.081042
• Samareh, H., Khoshrou, S.H., Shahriar, K., Ebadzadeh, M.M., Eslami, M., Optimization of a nonlinear model for predicting the ground vibration using the combinational particle swarm optimization-genetic algorithm, Journal of African Earth Sciences, 133 (2017), 36-45. https://doi.org/10.1016/j.jafrearsci.2017.04.029
• Satapathy, S.C., Murthy, J.V., Reddy, P.P., Misra, B.B., Dash, P.K., Panda, G., Particle swarm optimized multiple regression linear model for data classification,Applied Soft Computing, 9(2) (2009), 470-476. https://doi.org/10.1016/j.asoc.2008.05.007
• Sari, M., Tuna, C., Akogul, S., Prediction of tibial rotation pathologies using particle swarm optimization and K-means algorithms,Journal of Clinical Medicine, 7(4) (2018), 65. https://doi.org/10.3390/jcm7040065
• Shi, Y., Eberhart, R., A modi…ed particle swarm optimizer, In Evolutionary Computation Proceedings, 1998. IEEE World Congress on Computational Intelligence, The 1998 IEEE International Conference,, IEEE, (1998), 69-73. https://doi.org/10.1109/ICEC.1998.699146
• Kennedy, J., Eberhart, R., Particle swarm optimization, In Neural Networks, Proceedings, IEEE International Conference, 4 (1995), 1942-1948. http://doi.org/10.1109/ICNN.1995. 488968
• Yang, C., Simon, D., A new particle swarm optimization technique, 8th International Conference on Systems Engineering (ICSEng’05) (2005), 164-169. https://doi.org/10.1109/ ICSENG.2005.9
• Marini, F., Walczak, B., Particle swarm optimization (PSO). A tutorial, Chemometrics and Intelligent Laboratory Systems, 149 (2015), 153-165. https://doi.org/10.1016/j.chemolab. 2015.08.020
• Poli, R., Kennedy J., Blackwell, T., Particle swarm optimization, Swarm Intelligence, 1(1) (2007), 33-57. http://doi.org/10.1007/s11721-007-0002-0
• Hoque, M., Hoque, E., Kader, S.B., Risk factors for anaemia in pregnancy in rural KwaZuluNatal, South Africa: implication for health education and health promotion, South African Family Practice, 51(1) (2009), 68-72. https://doi.org/10.1080/20786204.2009.10873811
• Sari, M., Ahmad, A.A., Anemia modelling using the multiple regression analysis, International Journal of Analysis and Applications 17(5) (2019), 838-849. https://doi.org/10.28924/2291-8639-17-2019-838
• Alzaidi, K.M., Bayat, O., Uçan, O.N., A heuristic approach for optimal planning and operation of distribution systems, Journal of Optimization, 2018, (2018). https://doi.org/10.1155/2018/6258350
• Bai, Q., Analysis of particle swarm optimization algorithm,Computer and Information Science, 3(1) (2010), 180. https://doi.org/10.5539/cis.v3n1p180
• He, Y., Ma, W., Zhang, J., The Parameters Selection of PSO Algorithm influencing on performance of Fault Diagnosis, MATEC Web of Conferences, 63, 02019 (2016). https://doi.org/10.1051/matecconf/20166302019
• Wang, Y., Li, B., Yin, L., Wu, J., Wu, S., Liu, C.,Velocity-controlled particle swarm optimization (PSO) and its application to the optimization of transverse flux induction heating apparatus, Energies, 12(487) (2019). https://doi.org/10.3390/en12030487
• Seber, G.A., Wild, C.J., Nonlinear regression, John Wiley n& Sons, Hoboken, NJ. 2003.
• Rudolf, F.J., William, J.W., Ping, S., Regression analysis: statistical modeling of a response variable, Elsevier, USA, 2006.
• Mohanty, S.D., Particle swarm optimization and regression analysis-I, Astronomical Review, 7(2) (2012), 29-35. https://doi.org/10.1080/21672857.2012.11519700
• Ngwira, A., Kazembe, L.N., Analysis of severity of childhood anemia in Malawi: a Bayesian ordered categories model, Open Access Medical Statistics, 6 (2016), 9-20. https://doi.org/10.2147/OAMS.S95159
• Chen, Y.M., Miaou, S.G., A kalman filtering and nonlinear penalty regression approach for noninvasive anemia detection with palpebral conjunctiva images, Journal of Healthcare Engineering, (2017). https://doi.org/10.1155/2017/9580385
• Habyarimana, F., Zewotir, T., Ramroop, S., Structured additive quantile regression for assessing the determinants of childhood anemia in Rwanda, International Journal of Environmental Research and Public Health, 14(6) (2017), 652.https://doi.org/10.3390/ijerph14060652