Support Allergic Patients, using Models Found by Machine Learning Algorithms, to Improve their Quality of Life.
Keywords:
machine learning algorithms; food allergy; molecular allergy test; apriori algorithm.Abstract
Food allergy is a disease that negatively affects quality of life, and in some cases its impact is serious. Diagnosing food allergies prior to exposure to the allergen(s) has significant costs and results in overdiagnosis leading to the avoidance of food to which patients are not allergic. Discovering relationships between features of food allergy data would support patients by finding their food allergens and avoiding the use of costly diagnostics. This paper presents the potential of using machine learning algorithms in discovering these relationships. The data was collected by the medical laboratory Intermedica through tests performed by patients with food allergies. The apriori algorithm is applied to these data. The relationships discovered in our data are implemented in a software application, which also has an interface to enter data about new patients being screened for food allergies. The set of discovered relationships leads to the creation of a list of food allergens for a new patient, which helps them eliminate the molecular allergy test when it is not necessary and as a result, reduce financial costs. The model also supports patients by not eliminating foods that do not harm them, thereby not risking a nutritional deficit.
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L. K. Arruda, D. Solé, C. E. Baena-Cagnani, and C. K. Naspitz, “Risk factors for asthma and atopy,” Curr Opin Allergy Clin Immunol, vol. 5, no. 2, pp. 153–159, Apr. 2005, doi: 10.1097/01.all.0000162308.89857.6c.
P.A. Eigenmann, A.M. Calza, “Diagnosis of IgE-mediated food allergy among Swiss children with atopic dermatitis - PubMed.” Pediatr Allergy Immunol. vol. 11, no. 2, pp. 95-100, May. 2000, doi:10.1034/j.1399-3038.2000.00071.x
S. Romagosa Vilarnau, H.A. Brough, S. Schnadt, M. Podestà, R. Åsgård, E. Botjes, F.R. Thordardottir, R.W.R. Crevel, E.N.C. Mills, A. Muraro, S. Arasi, P. Chaslaridis, (2020), “1798|Food detectives: Quality of life, food allergen labelling and EU law” [Online]. Available:
https://onlinelibrary.wiley.com/doi/10.1111/all.14509
https://www.efanet.org/news/31-efa-projects/3914-efa-food-detectives-project
Allergen Bureau. Food Allergen. [Online]. Last accessed: 25.07.2022 Available: https://allergenbureau.net/food-allergens/
R. Meyer, “Nutritional disorders resulting from food allergy in children,” Pediatr Allergy Immunol, vol. 29, no. 7, pp. 689–704, Nov. 2018, doi: 10.1111/pai.12960.
T. Umasunthar, J. Leonardi-Bee, M. Hodes, P.J. Turner, C. Gore, P. Habibi, J.O. Warner, R.J. Boyle, “Incidence of food anaphylaxis in people with food allergy: a systematic review and meta-analysis,” Clin Exp Allergy, vol. 45, no. 11, pp. 1621–1636, Nov. 2015, doi: 10.1111/cea.12477.
R. S. Gupta, C. M. Warren, B. M. Smith, J. A. Blumenstock, J. Jiang, M. M. Davis, & K. C. Nadeau, “The Public Health Impact of Parent-Reported Childhood Food Allergies in the United States,” Pediatrics, vol. 142, no. 6, p. e20181235, Dec. 2018, doi: 10.1542/peds.2018-1235.
S. A. Rudders, S. A. Arias, and C. A. Camargo, “Trends in hospitalizations for food-induced anaphylaxis in US children, 2000-2009,” J Allergy Clin Immunol, vol. 134, no. 4, pp. 960-962.e3, Oct. 2014, doi: 10.1016/j.jaci.2014.06.018.
S. M. Jones and A. W. Burks, “Food Allergy,” N Engl J Med, vol. 377, no. 12, pp. 1168–1176, Sep. 2017, doi: 10.1056/NEJMcp1611971.
S. A. Bock, A. Muñoz-Furlong, and H. A. Sampson, “Further fatalities caused by anaphylactic reactions to food, 2001-2006,” J Allergy Clin Immunol, vol. 119, no. 4, pp. 1016–1018, Apr. 2007, doi: 10.1016/j.jaci.2006.12.622.
The European Academy of Allergy and Clinical Immunology, (2011) [Online]. Last accessed: 25.07.2022. Available:
http://hugin.info/146478/R/1490004/425677.pdf
R.S. Gupta, E.E. Springston, M.R. Warrier, B. Smith, R. Kumar, J. Pongracic, J.L. Holl, “The prevalence, severity, and distribution of childhood food allergy in the United States,” Pediatrics, vol. 128, no. 1, pp. e9-17, Jul. 2011, doi: 10.1542/peds.2011-0204.
The Nourished Child. (2019). Multiple Food Allergies: 7 Dangers for Children. [Online]. Last accessed 25.07.2022. Available:
https://thenourishedchild.com/multiple-food-allergies-7-real-nutrition-risks-kids/.
S. Clark, J. Espinola, S. A. Rudders, A. Banerji, and C. A. Camargo, “Frequency of US emergency department visits for food-related acute allergic reactions,” J Allergy Clin Immunol, vol. 127, no. 3, pp. 682–683, Mar. 2011, doi: 10.1016/j.jaci.2010.10.040.
R. Pawankar, “It’s time for an evolution,” Asia Pac Allergy, vol. 11, no. 1, p. e11, 2021, doi: 10.5415/apallergy.2021.11.e11.
R. Pawankar, G.W. Canonica, S.T. Holgate, R.F. Lockey, M. Blaiss, The WAO White Book on Allergy (Update. 2013) [Online] Last Accessed: 01.08.2022, Available: https://www.worldallergy.org/UserFiles/file/WhiteBook2-2013-v8.pdf
W. Loh and M. L. K. Tang, “The Epidemiology of Food Allergy in the Global Context,” Int J Environ Res Public Health, vol. 15, no. 9, p. E2043, Sep. 2018, doi: 10.3390/ijerph15092043.
S. H. Sicherer and H. A. Sampson, “Food allergy: A review and update on epidemiology, pathogenesis, diagnosis, prevention, and management,” Journal of Allergy and Clinical Immunology, vol. 141, no. 1, pp. 41–58, Jan. 2018, doi: 10.1016/j.jaci.2017.11.003.
NHS, National Health Service (UK). Food allergy: Diagnosis. [Online]. Last accessed: 25.07.2022. Available:
https://www.nhs.uk/conditions/food-allergy/diagnosis/
Food Allergy Research & Education (FARE). Blood Tests. [Online]. Last accessed: 25.07.2022. Available:
https://www.foodallergy.org/resources/blood-tests
E. Calamelli, L. Liotti, I. Beghetti, V. Piccinno, L. Serra, and P. Bottau, “Component-Resolved Diagnosis in Food Allergies,” Medicina (Kaunas), vol. 55, no. 8, p. E498, Aug. 2019, doi: 10.3390/medicina55080498
J. Flores Kim, N. McCleary, B. I. Nwaru, A. Stoddart, and A. Sheikh, “Diagnostic accuracy, risk assessment, and cost-effectiveness of component-resolved diagnostics for food allergy: A systematic review,” Allergy, vol. 73, no. 8, pp. 1609–1621, Aug. 2018, doi: 10.1111/all.13399.
DeepAI. Machine Learning. [Online]. Last accessed:25.07.2022. Available: Machine Learning Definition | DeepAI
Ian H. Witten, Eibe Frank, Mark A. Hall, Christopher Pal, “What’s it all about?” in Data Mining Practical Machine Learning Tools and Techniques, 4th ed. Burlington, Massachusetts, USA: Morgan-Kaufmann, 2017, pp. 3-9.
Jiawei Han, Micheline Kamber, Jian Pei, “Introduction” in Data Mining: Concepts and Techniques (The Morgan Kaufmann Series in Data Management Systems), 3rd ed. Burlington, Massachusetts, USA: Morgan-Kaufmann, 2011, pp. 1-24.
B. T.K., C. S. R. Annavarapu, and A. Bablani, “Machine learning algorithms for social media analysis: A survey,” Computer Science Review, vol. 40, p. 100395, May 2021, doi: 10.1016/j.cosrev.2021.100395.
J. Patil, H. Kadwe, P. Thakhre, S. Manna, and P. S. Gavhane, “Product Recommendation using Machine Learning Algorithm - A Better Appoarch,” International Journal of Engineering Research & Technology, vol. 8, no. 11, Dec. 2019, doi: 10.17577/IJERTV8IS110314.
J. Matuszewski and A. Rajkowski, “The use of machine learning algorithms for image recognition,” in Radioelectronic Systems Conference 2019, Feb. 2020, vol. 11442, pp. 412–422. doi: 10.1117/12.2565546.
I. Cinar and M. Koklu, “Classification of Rice Varieties Using Artificial Intelligence Methods”, Int J Intell Syst Appl Eng, vol. 7, no. 3, pp. 188–194, Sep. 2019.
Y. S. Taspinar, M. Koklu, and M. Altin, “Fire Detection in Images Using Framework Based on Image Processing, Motion Detection and Convolutional Neural Network”, Int J Intell Syst Appl Eng, vol. 9, no. 4, pp. 171–177, Nov. 2021.
I. A. Ozkan and M. Koklu, “Skin Lesion Classification using Machine Learning Algorithms”, Int J Intell Syst Appl Eng, vol. 5, no. 4, pp. 285–289, Dec. 2017.
J. Ive, “Natural Language Processing: A Machine Learning Perspective by Yue Zhang and Zhiyang Teng,” Computational Linguistics, vol. 48, no. 1, pp. 233–235, Mar. 2022, doi: 10.1162/coli_r_00423.
K. S. Patil, “A Survey on Machine Learning Techniques for Insurance Fraud Prediction,” Helix, vol. 8, no. 6, pp. 4358–4363, Oct. 2018, doi: 10.29042/2018-4358-4363.
D. Pavithra and J. A.N., “A Study On Machine Learning Algorithm In Medical Diagnosis,” International Journal of Advanced Research in Computer Science, vol. 9, Aug. 2018, doi: 10.26483/ijarcs.v9i4.6281.
I. Kumar, S. P. Singh, and Shivam, “Machine learning in bioinformatics,” in Bioinformatics, Elsevier, 2022, pp. 443–456. doi: 10.1016/B978-0-323-89775-4.00020-1.
P. Rai, S. Prabhumoye, P. Khattri, L. Sandhu, and S. Kamath S, “A Prototype of an Intelligent Search Engine Using Machine Learning Based Training for Learning to Rank,” Jun. 2014, vol. 27. doi: 10.1007/978-3-319-07353-8_9.
B. Henrique, V. Sobreiro, and H. Kimura, “Literature Review: Machine Learning Techniques Applied to Financial Market Prediction,” Expert Systems with Applications, vol. 124, Jun. 2019, doi: 10.1016/j.eswa.2019.01.012.
G. W. Bassel, E. Glaab, J. Marquez, M. J. Holdsworth, and J. Bacardit, “Functional Network Construction in Arabidopsis Using Rule-Based Machine Learning on Large-Scale Data Sets[C][W][OA],” Plant Cell, vol. 23, no. 9, pp. 3101–3116, Sep. 2011, doi: 10.1105/tpc.111.088153.
GECCO: Genetic and Evolutionary Computation Conference, Denver, Colorado, USA (July 20-24, 2016), Tutorials: Introducing rule-based machine learning: capturing complexity, [Online] Last accessed 25.07.2022, Available:
J. J. Koplin et al., “The Impact of Family History of Allergy on Risk of Food Allergy: A Population-Based Study of Infants,” Int J Environ Res Public Health, vol. 10, no. 11, pp. 5364–5377, Nov. 2013, doi: 10.3390/ijerph10115364.
K. Cheng, C. Zhang, H. Yu, X. Yang, H. Zou, and S. Gao, “Grouped SMOTE With Noise Filtering Mechanism for Classifying Imbalanced Data,” IEEE Access, vol. 7, pp. 170668–170681, 2019, doi: 10.1109/ACCESS.2019.2955086.
E. Frank and I. H. Witten, “Generating accurate rule sets without global optimization,” University of Waikato, Department of Computer Science, Working Paper, Jan. 1998. Accessed: Aug. 01, 2022. [Online]. Available: https://researchcommons.waikato.ac.nz/handle/10289/1047
S. Salzberg, “Book Review: C4.5: Programs for Machine Learning by J. Ross Quinlan. Morgan Kaufmann Publishers, Inc., 1993,” Machine Learning, 2004, doi: 10.1023/A:1022645310020.
R. Agrawal, and R. Srikant: Fast Algorithms for Mining Association Rules in Large Databases. In: 20th International Conference on Very Large Data Bases, 478-499, 1994.
B. Liu, W. Hsu, and Y. Ma, “Integrating Classification and Association Rule Mining,” In: Fourth International Conference on Knowledge Discovery and Data Mining, 80-86, 1998.
Jon Duckett, PHP & MySQL: Server-Side Web Development, Hoboken, New Jersey, USA: Wiley, 2022.
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