Hybrid of Smart System Model to Support the Service of Fertility Doctors in Handling In-Vitro Fertilization Patient Complaints
Abstract
The majority of In-Vitro Fertilization (IVF) patients immediately call a fertility doctor when they experience different symptoms than usual. However, the high workload makes fertility doctors unable to immediately provide recommendations to handle complaints of IVF patients, while the longer wait for recommendations from fertility doctors will increase the anxiety of IVF patients and high levels of anxiety affect the success rate of IVF programs. The Case-Based Reasoning (CBR) model has lower performance than the modified CBR model, and the CBR model adds to the workload of fertility doctors, namely having to handle the revision stage. To overcome these problems, the CBR model was modified by applying the Chris Case-Based Reasoning (CCBR) similarity formula and combining it with the Rule-Based Reasoning model. The results of performance measurements showed that the accuracy score increased to 47\% and the precision score remained 100\%, so the results of this modification of the CBR model are worthy of being recommended for application to a smart system for handling complaints of IVF patients.
References
Adeniyi, D., Wei, Z., and Yang, Y. Risk factors analysis and death prediction in some lifethreatening ailments using chi-square case-based reasoning (χ 2 cbr) model. Interdisciplinary Sciences: Computational Life Sciences 10 (2018), 854–874.
Agarwal, N., and Biswas, B. Doctor consultation through mobile applications in india: An overview, challenges and the way forward. Healthcare Informatics Research 26, 2 (2020), 153–158.
Akbulut, A., Ertugrul, E., and Topcu, V. Fetal health status prediction based on maternal clinical history using machine learning techniques. Computer methods and programs in biomedicine 163 (2018), 87–100.
Al Said, N., Gura, D., and Karlov, D. Efficiency of smart ai-based voice apps and virtual services operating with chatbots. Mendel 28, 2 (2022), 9–16.
Aldayel, M., and Benhidour, H. Product recommendation in case-based reasoning. In 2019 2nd International Conference on Computer Applications & Information Security (ICCAIS) (2019), IEEE, pp. 1–6.
Amami, R., et al. The use of an incremental learning algorithm for diagnosing covid-19 from chest x-ray images. Mendel 28, 1 (2022), 1–7.
Ashley, W. D., and Krause, A. Foundations of pygtk development. Foundations of PyGTK Development (2019), 317–44.
Aslzaker, M., et al. Effects of infertility stress, psychological symptoms, and quality of life on predicting success rate of ivf/icsi treatment in infertile women. Practice in Clinical Psychology 4, 4 (2016), 275–281.
Asri, H., Mousannif, H., and Al Moatassime, H. Reality mining and predictive analytics for building smart applications. Journal of Big Data 6 (2019), 1–25.
Avdeenko, T., and Makarova, E. Integration of case-based and rule-based reasoning through fuzzy inference in decision support systems. Procedia Computer Science 103 (2017), 447–453.
Bentaiba-Lagrid, M. B., et al. A casebased reasoning system for supervised classification problems in the medical field. Expert Systems with Applications 150 (2020), 113335.
Boivin, J., Bunting, L., Koert, E., ieng U, C., and Verhaak, C. Perceived challenges of working in a fertility clinic: a qualitative analysis of work stressors and difficulties working with patients. Human Reproduction 32, 2 (2017), 403–408.
Bras de Guimaraes, B., et al. Application of artificial intelligence algorithms to estimate the success rate in medically assisted procreation. Reproductive Medicine 1, 3 (2020), 181–194.
Brown, D., Aldea, A., Harrison, R., Martin, C., and Bayley, I. Temporal case-based reasoning for type 1 diabetes mellitus bolus insulin decision support. Artificial intelligence in medicine 85 (2018), 28–42.
Bui, D.-K., et al. A modified firefly algorithm artificial neural network expert system for predicting compressive and tensile strength of high-performance concrete. Construction and Building Materials 180 (2018), 320–333.
Capuzzi, E., et al. Is in vitro fertilization (ivf) associated with perinatal affective disorders? Journal of Affective Disorders 277 (2020), 271–278.
Christianto, P. A. New toeh+ p framework for the adoption of smart patient management system strategies at an ivf (in vitro fertilization) program provider hospital in central java province. International Journal of Information Technology and Business 2, 2 (2020), 1–7.
Christianto, P. A., Sediyono, E., and Sembiring, I. Case-based reasoning modifications for intelligent systems in handling in vitro fertilization (ivf) patients post embryo transfer. In 2020 International Seminar on Application for Technology of Information and Communication (iSemantic) (2020), IEEE, pp. 109–114.
Christianto, P. A., Sediyono, E., and Sembiring, I. Modification of case-based reasoning similarity formula to enhance the performance of smart system in handling the complaints of in vitro fertilization program patients. Healthcare Informatics Research 28, 3 (2022), 267–275.
Christianto, P. A., Sediyono, E., Sembiring, I., and Wijono, S. Intelligent system of handling in vitro fertilization (ivf) patients post embryo transfer to reduce the level of patient anxiety and help fertility doctors quickly answer patient questions. In Proceedings of the 1st International Conference on Electronics, Biomedical Engineering, and Health Informatics (2021), Springer, pp. 183–196.
Costa, A., Heras, S., Palanca, J., Jordan, J., Novais, P., and Julian, V. Argumentation schemes for events suggestion in an e-health platform. In Persuasive Technology: Development and Implementation of Personalized Technologies to Change Attitudes and Behaviors: 12th International Conference (2017), Springer, pp. 17–30.
Facchin, F., et al. Working with infertile couples seeking assisted reproduction: an interpretative phenomenological study with infertility care providers. Frontiers in Psychology 11 (2020), 586873.
Gdanska, P., Drozdowicz-Jastrzebska, E., Grzechocinska, B., Radziwon-Zaleska, M., Wegrzyn, P., and Wielgos, M. Anxiety and depression in women undergoing infertility treatment. Ginekologia polska 88, 2 (2017), 109–112.
Gnanasambanthan, S., and Datta, S. Early pregnancy complications. Obstetrics, Gynaecology & Reproductive Medicine 29, 2 (2019), 29–35.
Goldstein, C. A., Lanham, M. S., Smith, Y. R., and O’Brien, L. M. Sleep in women undergoing in´avitro fertilization: a pilot study. Sleep medicine 32 (2017), 105–113.
Gozuyesil, E., Karacay Yikar, S., and Nazik, E. An analysis of the anxiety and hopelessness levels of women during ivf-et treatment. Perspectives in Psychiatric Care 56, 2 (2020), 338–346.
Haimovici, F., et al. Stress, anxiety, and depression of both partners in infertile couples are associated with cytokine levels and adverse ivf outcome. American journal of reproductive immunology 79, 4 (2018), e12832.
Haleem, A., Javaid, M., and Khan, I. H. Current status and applications of artificial intelligence (ai) in medical field: An overview. Current Medicine Research and Practice 9, 6 (2019), 231–237.
Huang, L.-H., Kuo, C.-P., Lu, Y.-C., Lee, M.-S., and Lee, S.-H. Association of emotional distress and quality of sleep among women receiving in-vitro fertilization treatment. Taiwanese Journal of Obstetrics and Gynecology 58, 1 (2019), 168–172.
Iftikhar, P., Kuijpers, M. V., Khayyat, A., Iftikhar, A., and De Sa, M. D. Artificial intelligence: a new paradigm in obstetrics and gynecology research and clinical practice. Cureus 12, 2 (2020).
Indonesia, K. K. Konsil kedokteran indonesia no. 87 tahun 2020, 2020.
Klitzman, R. Infertility providers’ and patients’ views and experiences concerning doctor shopping in the usa. Human Fertility (2017).
Kumar, P., Sait, S. F., Sharma, A., and Kumar, M. Ovarian hyperstimulation syndrome. Journal of human reproductive sciences 4, 2 (2011), 70.
Lang, J., Zhang, B., Meng, Y., Du, Y., Cui, L., and Li, W. First trimester depression and/oranxiety disorders increase the risk of low birthweight in ivf offspring: a prospective cohort study. Reproductive BioMedicine Online 39, 6 (2019), 947–954.
Maia Bezerra, N. K., et al. Success of in vitro fertilization and its association with the levels of psychophysiological stress before and during the treatment. Health Care for Women International 42, 4-6 (2021), 420–445.
Malathi, D., et al. Hybrid reasoning-based privacy-aware disease prediction support system. Computers & Electrical Engineering 73 (2019), 114–127.
Moreira, M. W., Rodrigues, J. J., Kumar, N., Saleem, K., and Illin, I. V. Postpartum depression prediction through pregnancy data analysis for emotion-aware smart systems. Information Fusion 47 (2019), 23–31.
Octavius, G. S., Antonio, F., et al. Antecedents of intention to adopt mobile health (mhealth) application and its impact on intention to recommend: An evidence from indonesian customers. International journal of telemedicine and applications 2021 (2021).
Pontius, E., and Vieth, J. T. Complications in early pregnancy. Emergency Medicine Clinics 37, 2 (2019), 219–237.
Ramos-Gonzalez, J., et al. A cbr framework with gradient boosting based feature selection for lung cancer subtype classification. Computers in biology and medicine 86 (2017), 98–106.
Sapra, K., et al. Signs and symptoms associated with early pregnancy loss: findings from a population-based preconception cohort. Human Reproduction 31, 4 (2016), 887–896.
Sapra, K. J., et al. Signs and symptoms of early pregnancy loss: a systematic review. Reproductive Sciences 24, 4 (2017), 502–513.
Saraiva, R., Perkusich, M., Silva, L., Almeida, H., Siebra, C., and Perkusich, A. Early diagnosis of gastrointestinal cancer by using case-based and rule-based reasoning. Expert Systems with Applications 61 (2016), 192–202.
Schroeder, J., Karkar, R., Fogarty, J., Kientz, J. A., Munson, S. A., and Kay, M. A patient-centered proposal for bayesian analysis of self-experiments for health. Journal of healthcare informatics research 3 (2019), 124–155.
Singh, S. T. A. Impact of genetic optimization on the prediction performance of case-based reasoning algorithm in liver disease. International Journal of Performability Engineering 13, 4 (2017), 348.
Song, K., De Jonckheere, J., Zeng, X., Koehl, L., Yuan, X., and Zhao, X. Development of a data-based interactive medical expert system for supporting pregnancy consultations: General architecture and methodology. IFACPapersOnLine 52, 19 (2019), 67–72.
Su, Y., Yang, S., Liu, K., Hua, K., and Yao, Q. Developing a case-based reasoning model for safety accident pre-control and decision making in the construction industry. International journal of environmental research and public health 16, 9 (2019), 1511.
Tartaglia, E., et al. Telemedicine: A cornerstone of healthcare assistance during the sarscov2 pandemic outbreak but also a great opportunity for the near future. Smart Health 26 (2022), 100324.
Thike, P. H., Xu, Z., Cheng, Y., Jin, Y., and Shi, P. Materials failure analysis utilizing rulecase based hybrid reasoning method. Engineering Failure Analysis 95 (2019), 300–311.
Torrent-Fontbona, F., and L´opez, B. Personalized adaptive cbr bolus recommender system for type 1 diabetes. IEEE journal of biomedical and health informatics 23, 1 (2018), 387–394.
Vaishya, R., Javaid, M., Khan, I. H., and Haleem, A. Artificial intelligence (ai) applications for covid-19 pandemic. Diabetes & Metabolic Syndrome: Clinical Research & Reviews 14, 4 (2020), 337–339.
Yang, C. C. Explainable artificial intelligence for predictive modeling in healthcare. Journal of healthcare informatics research 6, 2 (2022), 228–239.
Yang, Y., Zhang, X., and Lee, P. K. Improving the effectiveness of online healthcare platforms: An empirical study with multi-period patient-doctor consultation data. International Journal of Production Economics 207 (2019), 70–80.
Yuan, Z. Intelligent decision support system development technology of automotive mechanical system. In International Conference on Education, Management and Computing Technology (ICEMCT-16) (2016), Atlantis Press, pp. 1373–1377.
Zhou, F.-j., Cai, Y.-n., and Dong, Y.-z. Stress increases the risk of pregnancy failure incouples undergoing ivf. Stress 22, 4 (2019), 414–420.
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