An Effective Prediction Approach for the Management of Children Victims of Road Accidents.
DOI:
https://doi.org/10.9781/ijimai.2024.02.001Keywords:
Case Based Reasoning, Data Mining, Decision Tree, Medical Decision Making Approach, Predictive Modelling, Road Accident, Selection of Relevant Attributes, Traumatic Brain InjuriesAbstract
Road traffic generates a considerable number of accidents each year. The management of injuries caused by these accidents is becoming a real public health problem. Faced with this latter, we propose a new clinical decision making approach based on case-based reasoning (CBR) and data mining (DM) techniques to speed up and improve the care of an injured child. The main idea is to preprocess the dataset before using K Nearest Neighbor (KNN) Classification Model. In this paper, an efficient predictive model is developed to predict the admission procedure of a child victim of a traffic accident in pediatric intensive care units. The evaluation of the proposed model is conducted on a real dataset elaborated by the authors and validated by statistical analysis. This novel model executes a selection of relevant attributes using data mining technique and integrates a CBR system to retrieve similar cases from an archive of cases of patients successfully treated with the proposed treatment plan. The results revealed that the proposed approach outperformed other models and the results of previous studies by achieving an accuracy of 91.66%.
Downloads
References
T. Thim, N. H. V. Krarup, E. L. Grove, C. V. Rohde, B. Løfgren, “Initial assessment and treatment with the airway, breathing, circulation, disability, exposure (abcde) approach,” International journal of general medicine, vol. 5, p. 117, 2012.
P. Andritsos, I. Jurisica, J. I. Glasgow, “Case-based reasoning for biomedical informatics and medicine,” Springer Handbook of Bio-/Neuroinformatics, pp. 207–221, 2014.
S. S. R. Abidi, S. Manickam, “Leveraging xml-based electronic medical records to extract experiential clinical knowledge: An automated approach to generate cases for medical case-based reasoning systems,” International Journal of Medical Informatics, vol. 68, no. 1-3, pp. 187–203, 2002.
K. C. A. D. Borges, I. F. de Barcelos Tronto, R. de Aquino Lopes, J. D. S. da Silva, “A methodology for preprocessing data for application of case based reasoning,” in 2012 XXXVIII Conferencia Latinoamericana En Informatica (CLEI), 2012, pp. 1–8, IEEE.
N. Choudhury, S. A. Begum, “A survey on case- based reasoning in medicine,” International Journal of Advanced Computer Science and Applications, vol. 7, no. 8, pp. 136–144, 2016.
F. Saadi, B. Atmani, F. Henni, “Improving retrieval performance of case based reasoning systems by fuzzy clustering,” International Journal of Interactive Multimedia and Artificial Intelligence, (2023), doi: http://dx.doi.org/10.9781/ijimai.2023.07.002.
Y. Guo, J. Hu, Y. Peng, “Research of new strategies for improving cbr system,” Artificial Intelligence Review, vol. 42, pp. 1–20, 2014.
R. Bellazzi, B. Zupan, “Predictive data mining in clinical medicine: current issues and guidelines,” International journal of medical informatics, vol. 77, no. 2, pp. 81–97, 2008.
F. Saadi, B. Atmani, F. Henni, “Integration of datamining techniques into the cbr cycle to predict the result of immunotherapy treatment,” in 2019 International Conference on Computer and Information Sciences (ICCIS), 2019, pp. 1–5, IEEE.
B. N. Barigou, F. Barigou, C. Benchehida, B. Atmani, G. Belalem, “Thedesign of a cloud-based clinical decision support system prototype: management of drugs intoxications in childhood,” in Research Anthology on Decision Support Systems and Decision Management in Healthcare, Business, and Engineering, IGI Global, 2021, pp. 387–409.
L. J. Hoeksema, A. Bazzy-Asaad, E. A. Lomotan, D. E. Edmonds, G. Ramírez-Garnica, R. N. Shiffman, L. I. Horwitz, “Accuracy of a computerized clinical decision-support system for asthma assessment and management,” Journal of the American Medical Informatics Association, vol. 18, no. 3, pp. 243–250, 2011.
S. Walczak, S. R. Okuboyejo, “An artificial neural network classification of prescription nonadherence,” International Journal of Healthcare Information Systems and Informatics (IJHISI), vol. 12, no. 1, pp. 1–13, 2017.
E. K. Hashi, M. S. U. Zaman, M. R. Hasan, “An expert clinical decision support system to predict disease using classification techniques,” in 2017 International conference on electrical, computer and communication engineering (ECCE), 2017, pp. 396–400, IEEE.
Y.-S. Chang, C.-T. Fan, W.-T. Lo, W.-C. Hung, S.-M. Yuan, “Mobile cloud-based depression diagnosis using an ontology and a bayesian network,” Future Generation Computer Systems, vol. 43, pp. 87–98, 2015.
F. Z. Benhacine, B. Atmani, F. Z. Abdelouhab, “Contribution to the association rules visualization for decision support: A combined use between boolean modeling and the colored 2d matrix,” International Journal of Interactive Multimedia and Artificial Intelligence, vol. 5, no. 5, pp. 38–48, 2019.
E. R. Bareiss, B. W. Porter, C. C. Wier, “Protos: An exemplar-based learning apprentice,” in Machine learning, Elsevier, 1990, pp. 112–127.
C. Cândea, G. Cândea, Z. B. Constantin, “Ardocare - a collaborative medical decision support system,” Procedia Computer Science, vol. 162, pp. 762–769, 2019.
V. Tang, P. K. Y. Siu, K. L. Choy, H. Y. Lam, G. T. S. Ho, C. K. M. Lee, Y. P. Tsang, “An adaptive clinical decision support system for serving the elderly with chronic diseases in healthcare industry,” Expert Systems, vol. 36, no. 2, p. e12369, 2019.
D. S. Kumar, G. Sathyadevi, S. Sivanesh, “Decision support system for medical diagnosis using data mining,” International Journal of Computer Science Issues (IJCSI), vol. 8, no. 3, p. 147, 2011.
E. Alickovic, A. Subasi, “Medical decision support system for diagnosis of heart arrhythmia using dwt and random forests classifier,” Journal of medical systems, vol. 40, no. 4, pp. 1–12, 2016.
A. Yadollahpour, J. Nourozi, S. A. Mirbagheri, E. Simancas-Acevedo, F. R. Trejo-Macotela, “Designing and implementing an anfis based medical decision support system to predict chronic kidney disease progression,” Frontiers in physiology, p. 1753, 2018.
F. Hamedan, A. Orooji, H. Sanadgol, A. Sheikhtaheri, “Clinical decision support system to predict chronic kidney disease: A fuzzy expert system approach,” International journal of medical informatics, vol. 138, p. 104134, 2020.
Y. Guo, K. Wu, “Research on case retrieval of bayesian network under big data,” Data & Knowledge Engineering, vol. 118, pp. 1–13, 2018.
A. Mansoul, B. Atmani, “Combining multi-criteria analysis with cbr for medical decision support,” Journal of Information Processing Systems, vol. 13, no. 6, pp. 1496– 1515, 2017.
H. Benfriha, B. Atmani, F. Barigou, F. Henni, B. Khemliche, S. Fatima, A. Douah, Z. Z. Addou, “Improving cbr retrieval process through multilabel text categorization for health care of childhood traumatic brain injuries in road accident,” in Proceedings of Sixth International Congress on Information and Communication Technology, 2022, pp. 721–731, Springer.
A. Mansoul, B. Atmani, “Clustering to enhance case- based reasoning,” in Modelling and Implementation of Complex Systems, Springer, 2016, pp. 137–151.
N. Malviya, N. Choudhary, K. Jain, “Content based medical image retrieval and clustering based segmentation to diagnose lung cancer,” Advances in Computational Sciences and Technology, vol. 10, no. 6, pp. 1577–1594, 2017.
F. Saadi, B. Atmani, F. Henni, “Integration of fuzzy clustering into the case base reasoning for the prediction of response to immunotherapy treatment,” in Mediterranean Conference on Pattern Recognition and Artificial Intelligence, 2019, pp. 192–206, Springer.
M. Benamina, B. Atmani, S. Benbelkacem, “Diabetes diagnosis by case-based reasoning and fuzzy logic,” International Journal of Interactive Multimedia and Artificial Intelligence, vol. 5, no. 3, pp. 72–81, 2018.
M. A. Hall, “Correlation-based feature selection for machine learning,” 1999.
H. Feuillâtre, V. Auffret, M. Castro, H. Le Breton, M. Garreau, P. Haigron, “Study of similarity measures for case-based reasoning in transcatheter aortic valve implantation,” in 2017 Computing in Cardiology (CinC), 2017, pp. 1–4, IEEE.
J. Jarmulak, S. Craw, R. Rowe, “Genetic algorithms to optimise cbr retrieval,” in European Workshop on Advances in Case-Based Reasoning, 2000, pp. 136–147, Springer.
S. B. Ayed, Z. Elouedi, E. Lefevre, “Managing uncertainty during cbr systems vocabulary maintenance using relational evidential c-means,” in 2020 International Multi-Conference on:“Organization of Knowledge and Advanced Technologies”(OCTA), 2020, pp. 1–10, IEEE.
E. G. Addisu, A. S. Boltena, S. Y. Amare, “Case-based reasoning framework for malaria diagnosis,” I. J. Information Technology and Computer Science, vol. 6, pp. 31-48, 2020.
F. Henni, B. Atmani, F. Atmani, F. Saadi, “Improving coronary artery disease prediction: Use of random forest, feature importance and case-based reasoning,” International Journal of Decision Support System Technology (IJDSST), vol. 15, no. 1, pp. 1–17, 2023.
M. D. Von Atzingen, D. R. C. Schmidt, E. A. P. M. Nonino, “Elaboration and application of an evaluation instrument in the immediate postoperative period, based on the advanced trauma life support protocol,” Acta Paulista de Enfermagem, vol. 21, pp. 616–623, 2008.
U. Stańczyk, B. Zielosko, G. Baron, “Discretisation of conditions in decision rules induced for continuous data,” PloS one, vol. 15, no. 4, p. e0231788, 2020.
F. Wilcoxon, “Individual comparisons by ranking methods,” in Breakthroughs in statistics, Springer, 1992, pp. 196–202.
A. Smiti, Z. Elouedi, “Using clustering for maintaining case based reasoning systems,” in 2013 5th International Conference on Modeling, Simulation and Applied Optimization (ICMSAO), 2013, pp. 1–6, IEEE.
H. Sun, X. Hu, “Attribute selection for decision tree learning with class constraint,” Chemometrics and Intelligent Laboratory Systems, vol. 163, pp. 16–23, 2017.
J. R. Quinlan, “Induction of decision trees,” Machine learning, vol. 1, pp. 81–106, 1986.
A. G. Karegowda, A. Manjunath, M. Jayaram, “Comparative study of attribute selection using gain ratio and correlation based feature selection,” International Journal of Information Technology and Knowledge Management, vol. 2, no. 2, pp. 271–277, 2010.
J. K. Han, M. Kamber, “Data mining: Concepts and techniques,” Morgan Kaufmann, 2001.
I. Ragnemalm, “The euclidean distance transform in arbitrarydimensions,” Pattern Recognition Letters, vol. 14, no. 11, pp. 883–888, 1993.
J. Ahn, S.-H. Ji, S. J. Ahn, M. Park, H.-S. Lee, N. Kwon, E.-B. Lee, Y. Kim, “Performance evaluation of normalization-based cbr models for improving construction cost estimation,” Automation in Construction, vol. 119, p. 103329, 2020.
M. T. Rezvan, A. Z. Hamadani, A. Shalbafzadeh, “Case-based reasoning for classification in the mixed data sets employing the compound distance methods,” Engineering Applications of Artificial Intelligence, vol. 26, no. 9, pp. 2001–2009, 2013.
A. Idri, A. Abran, T. Khoshgoftaar, “Fuzzy analogy: A new approach for software cost estimation,” in International Workshop on Software Measurement, 2001, pp. 28–29, Citeseer.
D. Chicco, G. Jurman, “Machine learning can predict survival of patients
with heart failure from serum creatinine and ejection fraction alone,” BMC medical informatics and decision making, vol. 20, no. 1, pp. 1–16, 2020.
F. Henni, Composition Dynamique de Services Web par Apprentissage Artificiel. PhD dissertation, Université Oran 1, 2015.
C. B. D. Newman, C. Merz, “UCI repository of machine learning databases,” 1998. [Online]. Available: http://www.ics.uci.edu/„mlearn/MLRepository.html.
S. Zhang, C. Zhang, Q. Yang, “Data preparation for data mining,” Applied artificial intelligence, vol. 17, no. 5- 6, pp. 375–381, 2003.
Downloads
Published
-
Abstract319
-
PDF110
Submit Article
Information
