ICSI protocol advisor: A decision support system for infertility protocol suggestion

Document Type : Research Article

Authors

Department of Information Technology, Faculty of Industrial Engineering, K.N. Toosi University of Technology, Tehran, Iran.

Abstract

Intra-Cytoplasmic Sperm Injection (ICSI) is one of the most common infertility treatments in which ovarian stimulation is carried out to extract the eggs from the ovaries. There are three, short, long, and pure treatment protocols of ovarian stimulation that vary by the type of medicine, the dosage of medicine, and the treatment term. Today, physicians choose an appropriate treatment protocol based on the patient's condition, such as age, and hormonal condition. This could be a relatively subjective and inaccurate method, particularly if the physician is not highly experienced. The present study investigates whether a decision support system can propose a more objective treatment protocol based on the patients’ data and data mining methods like logistic regression, decision tree, and Support Vector Machines (SVM). Such a system draws upon classification methods to propose proper treatment protocols for ICSI. Moreover, a separate module was developed to calculate the success rate of the proposed protocols. The system was tested with real data of treated patients at a Hospital in Tehran, Iran. The results showed the proposed system can predict the most proper treatment protocol with an accuracy of 81.90%. The proposed system can help inexperienced physicians to feel more confident about their advice.

Keywords

Main Subjects


References:
1.Infertility definitions and terminology. (2016, April 07,2019). Sexual and reproductive health. Retrieved 14December (2018).
2.Mascarenhas, M.N., Flaxman, S.R., Boerma, T., et al.“National, regional, and global trends in infertilityprevalence since 1990: A Systematic analysis of 277 healthsurveys” PLoS Medicine, 9(12), e1001356 (2012). https://doi.org/10.1371/journal.pmed.1001356
3.Bai, C.-F., Sun, J., Li, J., et al. “Gender differences in factors associated with depression in infertility patients” Journal ofAdvanced Nursing, 75(12), pp. 3515–3524 (2019).https://doi.org/10.1111/jan.14171
4.Figueira, J.R., Almeida-Dias, J., Matias, S., et al. “ElectreTri-C, a multiple criteria decision aiding sorting modelapplied to assisted reproduction,” International Journal ofMedical Informatics, 80(4), pp. 262–273 (2011). https://doi.org/10.1016/j.ijmedinf.2010.12.001
5.Mustafa, M., Sharifa, A.M., Hadi, J., et al. “Male andfemale infertility: Causes, and management” IOSR Journal of Dental and Medical Sciences, 18(9), pp. 27–32 (2019). https://doi.org/10.9790/0853-1809132732
6.Nagórska, M., Bartosiewicz, A., Obrzut, B., et al.“Gender differences in the experience of infertilityconcerning polish couples: Preliminary research”International Journal of Environmental Research andPublic Health, 16(13), 2337 (2019).https://doi.org/10.3390/ijerph16132337
7.Rouchou, B. “Consequences of infertility in developingcountries,” Perspectives in Public Health, 133(3), pp.174–179 (2013).https://doi.org/10.1177/1757913912472415
8.Gomathy, E., Radhika, K., Shivanagoud Patil, S., et al.“Knowledge and attitude of infertile couples attendingrural tertiary care centre” Indian Journal of Obstetricsand Gynecology Research, 7(2), pp. 177–181 (2020).https://doi.org/10.18231/j.ijogr.2020.037
9.Zarif Golbar Yazdi, H., Aghamohammadian Sharbaf, H., Kareshki, H., et al. “Infertility and psychological andsocial health of Iranian infertile women: A systematicreview,” Iranian Journal of Psychiatry, 15(1), pp. 67-79(2020). https://doi.org/10.18502/ijps.v15i1.2441
10.Nasim, S., Bilal, S., and Qureshi, M. “Psycho-socialaspects of infertility-a review of current trends” TheProfessional Medical Journal, 26(9), pp. 1537–1541(2019).https://doi.org/10.29309/tpmj/2019.26.09.4019
11.Hasanpoor-Azghdy, S.B., Simbar, M., and Vedadhi, A.“The social consequences of infertility among Iranianwomen: A qualitative study” International Journal ofFertility and Sterility, 8(4), pp. 409–420 (2015).https://doi.org/10.22074/ijfs.2015.4181
12.Shreffler, K.M., Gallus, K.L., Peterson, B., et al.“Couples and infertility” The Handbook of SystemicFamily Therapy, pp. 385–406 (2020).https://doi.org/10.1002/9781119438519.ch76
13.Chen, Sh., Wang, T., Zhang, S., et al. “Associationbetween infertility treatment and perinatal depressivesymptoms: A meta-analysis of observational studies”Journal of Psychosomatic Research, 120, pp. 110–117(2019). https://doi.org/10.1016/j.jpsychores.2019.03.016
14.Kononenko, I. “Machine learning for medical diagnosis:history, state of the art and perspective”, ArtificialIntelligence in Medicine, 23(1), pp. 89–109 (2001). https://doi.org/10.1016/s0933-3657(01)00077-x
15.AbdiShahshahani, M., Torabi, M., and Kazemi, A.“Investigating related factors to psychologicalsymptoms of infertile couples undergoing assistedreproductive treatment”, Directory of Open Access Journals, 9, p. 21 (2020). https://doi.org/10.4103/jehp.jehp_412_19
16.Saremi, A., Introduction to Infertility (2006).
17.Kaplan, B. “Evaluating informatics applications—clinical decision support systems literature review”,International Journal of Medical Informatics, 64(1), pp.15–37 (2001). https://doi.org/10.1016/s1386-5056(01)00183-6
18.Çomak, E., Arslan, A., and Türkoğlu, İ. “A decisionsupport system based on support vector machines fordiagnosis of the heart valve diseases”, Computers inBiology and Medicine, 37(1), pp. 21–27 (2007). https://doi.org/10.1016/j.compbiomed.2005.11.002
19.Barnato, A.E., Llewellyn-Thomas, H.A., Peters, E.M.,et al., “Communication and decision making in cancercare: Setting research priorities for decisionsupport/patients’ decision aids”, Medical DecisionMaking, 27(5), pp. 626–634 (2007).https://doi.org/10.1177/0272989x07306788
20.Belacel, N. “Multicriteria assignment methodPROAFTN: Methodology and medical application”,European Journal of Operational Research, 125(1), pp.175–183 (2000). https://doi.org/10.1016/s0377-2217(99)00192-7
21.Das, R., Turkoglu, I., and Sengur, A. “Effectivediagnosis of heart disease through neural networksensembles”, Expert Systems with Applications, 36(4),pp. 7675–7680 (2009).https://doi.org/10.1016/j.eswa.2008.09.013
22.Homaeinezhad, M.R., Tavakkoli, E., Afshar, A., et al.“Neuro-ANFIS architecture for ECG rhythm-typerecognition using different QRS geometrical-basedfeatures”, Iranian Journal of Electrical and ElectronicEngineering, 7(2), pp. 70–83 (2011). https://www.sid.ir/EN/VEWSSID/J_pdf/106520110201pdf
23.Jaspers, M.W.M., Vandenbos, C., Heinen, R.C., et al.“Development of a national protocol to screen Dutchcancer survivors on late cancer treatment effects”,International Journal of Medical Informatics, 76(4), pp.297–305 (2007).https://doi.org/10.1016/j.ijmedinf.2006.02.002
24.Jurisica, I., Mylopoulos, J., Glasgow, J., et al. “Case-based reasoning in IVF: prediction and knowledgemining”, Artificial Intelligence in Medicine, 12(1), pp.1–24 (1998). https://doi.org/10.1016/s0933-3657(97)00037-7
25.Lehtinen, J.-Ch., Forsström, J., Koskinen, P., et al.“Visualization of clinical data with neural networks,Case study: polycystic ovary syndrome”, InternationalJournal of Medical Informatics, 44(2), pp. 145–155(1997). https://doi.org/10.1016/s1386-5056(96)01265-8
26.Manna, C., Nanni, L., Lumini, A., et al. “Artificialintelligence techniques for embryo and oocyteclassification”, Reproductive BioMedicine Online,26(1), pp. 42–49 (Jan 2013).https://doi.org/10.1016/j.rbmo.2012.09.015
27.West, D., Mangiameli, P., Rampal, R., et al. “Ensemblestrategies for a medical diagnostic decision supportsystem: A breast cancer diagnosis application”,European Journal of Operational Research, 162(2), pp.532–551 (2005).https://doi.org/10.1016/j.ejor.2003.10.013
28.Yan, H., Jiang, Y., Zheng, J., et al. “A multilayerperceptron-based medical decision support system forheart disease diagnosis”, Expert Systems withApplications, 30(2), pp. 272–281 (2006).https://doi.org/10.1016/j.eswa.2005.07.022
29.Lashkari, A. and Firouzmand, M. “Developing a toolboxfor clinical preliminary breast cancer detection indifferent views of thermogram images using a set ofoptimal supervised classifiers”, Scientia Iranica, 25(3),pp. 1545-1560 (2017). https://doi.org/10.24200/sci.2017.4362
30.Rahimi Damirchi-Darasi, S., Fazel Zarandi, M.H.,Turksen, I.B., et al. “Type-2 fuzzy rule-based expertsystem for diagnosis of spinal cord disorders”, ScientiaIranica, 26(1), pp. 455-471 (2019).https://doi.org/10.24200/sci.2018.20228
31.Karimizadeh, A., Vali, M., and Modaresi, M.R.“Infection detection in cystic fibrosis patients based ontunable Q-factor wavelet transform of respiratory soundsignal and ensemble decision”, Scientia Iranica, 29(4),pp. 2014-2028 (2022).https://doi.org/10.24200/sci.2020.55468.4242
32.Moradi, M., Modarres, M., and Sepehri, M.M.,“Detecting factors associated with polypharmacy ingeneral practitioners’ prescriptions: A data miningapproach”, Scientia Iranica, 29(6), pp. 3489-3504 (2022).https://doi.org/10.24200/sci.2020.55569.4283
33.Abu-Naser, S.S. and Alhabbash, M.I. “Male infertilityexpert system diagnoses and treatment”, AmericanJournal of Innovative Research and Applied Sciences,2(4), pp. 181-192 (2016).
34.Desouza K.C. and Jacob, B. “Big data in the publicsector: Lessons for practitioners and scholars”,Administration and Society, 49(7), pp. 1043–1064(2014). https://doi.org/10.1177/0095399714555751
35.Letterie, G., MacDonald, A., and Shi, Z. “An artificialintelligence platform to optimize workflow during ovarian stimulation and IVF: process improvement and outcome-based predictions”, Reproductive BioMedicine Online, 44(2), pp. 254–260 (2022). https://doi.org/10.1016/j.rbmo.2021.10.006
36.Morris, G.C., Stewart, C.M.W., Schoeman, S.A., et al.“A cross-sectional study showing differences in theclinical diagnosis of pelvic inflammatory diseaseaccording to the experience of clinicians: implicationsfor training and audit”, Sexually Transmitted Infections,90(6), pp. 445–451 (2014). https://doi.org/10.1136/sextrans-2014-051646
37.Carraccio, C.L., Benson, B.J., Nixon, L.J., et al. “Fromthe educational bench to the clinical bedside:Translating the dreyfus developmental model to thelearning of clinical skills”, Academic Medicine, 83(8),pp. 761–767 (2008). https://doi.org/10.1097/acm.0b013e31817eb632
38.Eva, K.W. “What every teacher needs to know aboutclinical reasoning”, Medical Education, 39(1), pp. 98–106 (2005). https://doi.org/10.1111/j.1365-2929.2004.01972.x
39.Han, J., Kamber, M., and Pei, J., Data Mining: Conceptsand Techniques, Third Edition, Waltham: MorganKaufmann Publishers, pp. 1–38 (2012). https://doi.org/10.1016/b978-0-12-381479-1.00001-0
40.Wu, X., Kumar, V., and Ross Quinlan, J., et al. “Top 10algorithms in data mining”, Knowledge and InformationSystems, 14, pp. 1–37 (2007).https://doi.org/10.1007/s10115-007-0114-2
41.Field, A., Discovering Statistics using SPSS, SagePublications (2009).
42.Fonarow, G.C. “Risk stratification for in-hospitalmortality in acutely decompensated heart failureclassification and regression tree analysis”, JAMA,293(5), 572 (2005).https://doi.org/10.1001/jama.293.5.572
Volume 32, Issue 8
Transactions on Industrial Engineering
March and April 2025 Article ID:5005
  • Receive Date: 29 October 2020
  • Revise Date: 04 September 2021
  • Accept Date: 18 April 2022