Research Article
BibTex RIS Cite

Year 2019, Volume: 2 Issue: 2, 8 - 12, 28.12.2019

Sevinç İlhan Omurca Ekin Ekinci Bengisu Çakmak Selim Gizem Özkan

Abstract

References

  • [1] C. Lagor, W. P. Lord, N. W. Chbat, J. D. Schaffer, and T. Wendler, "Advances in Healthcare Technology." Netherlands: Springer, 2006, ch. 22.
  • [2] R. Pawankari, "Allergic diseases and asthma: a global public health concern and a call to action." World Allergy Organ J, vol. 7, pp. 1-3, May 2014.
  • [3] A. Mari, E. Scala, P. Palazzo, S. Ridolfi, D. Zennaro, and G. Carabella, "Bioinformatics applied to allergy: Allergen databases, from collecting sequence information to data integration. The Allergome platform as a model." Cell Immunol., vol. 244, no. 2, pp. 97-100, Dec. 2006.
  • [4] G. Devereux, "The increase in the prevalence of asthma and allergy: food for thought." Nat Rev Immunol., vol. 6, no. 11, pp. 869-874, Nov. 2006.
  • [5] K. Kadam, S. Sawant, V. K. Jayaraman, and U. Kulkarni-Kale, "Bioinformatics–Updated Features and Applications." London, UK: IntechOpen, 2016, ch. 4.
  • [6] A. Zorzet, M. Gustafsson, and U. Hammerling, "Prediction of Food Protein Allergenicity: A Bio-informatic Learning Systems Approach." In Silico Biol., vol. 2, no. 4, pp. 525-534, 2002.
  • [7] D. Soeria-Atmadja, A. Zorzet, M. G. Gustafsson, and U. Hammerling, "Statistical Evaluation of Local Alignment Features Predicting Allergenicity Using Supervised Classification Algorithms." Int Arch Allergy Immunol., vol. 133, no. 2, pp. 101-112, Feb. 2004.
  • [8] I. Dimitrov, L. Naneva, I. Bangov, and I. Doytchinova, "Allergenicity prediction by artificial neural network." J Chemometr., vol. 28, no. 4, pp. 282-286, Jan. 2014.
  • [9] H. X. Dang, C. B. Lawrence, "Allerdictor: fast allergen prediction using text classification techniques." Bioinformatics, vol. 30, no. 8, pp. 1120-1128, Apr. 2014.
  • [10] H. F. Ng, H. M. Fathoni, and I. C. Chen, "Prediction of allergy symptoms among children in Taiwan using data mining," 2009.
  • [11] G. K. Zewdie, D. J. Lary, E. Levetin, and G. F. Garuma, "Applying Deep Neural Networks and Ensemble Machine Learning Methods to Forecast Airborne Ambrosia Pollen." Int J Environ Res Public Health., vol. 16, no. 11, Jun. 2019.
  • [12] S. Fontenella, C. Frainay, C. S. Murray, A. Smpson, and A. Custovic, "Machine learning to identify pairwise interactions between specific IgE antibodies and their association with asthma: A cross-sectional analysis within a population-based birth cohort." PLoS Med., vol. 15, no. 11, pp. 1-22, Nov. 2018. [Online]. Available: https://doi.org/10.1371/journal.pmed.1002691
  • [13] K. Lee, A. Agrawal, and A. Choudhary, "Mining Social Media Streams to Improve Public Health Allergy Surveillance." in Proc. 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, Paris, 2015, pp. 815–822.
  • [14] J. J. Christopher, H. K. Nehemiah, and A. Kannan, "A clinical decision support system for the diagnosis of Allergic Rhinitis based on intradermal skin tests." Comput Biol Med., vol. 65, pp. 76-84, Oct. 2015.
  • [15] J. R. Quinlan, "Induction of decision trees." Mach Learn., vol. 1, no. 1, pp. 81-106, Mar. 1986.
  • [16] M. S. Chen, J. Han, and P. S. Yu, "Data Mining: An Overview from a Database Perspective." IEEE T Knowl Data En., vol. 8, no. 6, pp. 866-883, Dec. 1996.
  • [17] H. Zhuang, Y. Ni, and S. Kokot, "Combining HPLC–DAD and ICP-MS data for improved analysis of complex samples: Classification of the root samples from Cortex." Chemometr Intell Lab., vol. 135, pp. 183-191, July 2014.
  • [18] C. J. C. Burges, "A Tutorial on Support Vector Machines for Pattern Recognition." Data Min Knowl Disc., vol. 2, no. 2, pp. 121-167, June 1998.
  • [19] L. Breiman, "Random Forests." Mach Learn., vol. 45, no. 1, pp. 5-32, Oct. 2001.
  • [20] E. Ekinci, S. İlhan Omurca, and N. Acun, "A Comparative Study on Machine Learning Techniques Using Titanic Dataset." in Proc. 7th International Conference on Advanced Technologies, Antalya, 2018, pp. 411-416.
  • [21] P. Geurts, D. Ernst, and L. Wehenkel, "Extremely randomized trees." Mach Learn., vol. 63, no. 1, pp. 3-42, Apr. 2006.
  • [22] Z. H. Kilimci and S. Omurca, "Enhancement of the Heuristic Optimization Based Extended Space Forests with Classifier Ensembles." Int Arab J Inf Techn., vol. 17, no. 2, Mar. 2020.
  • [23] H. G. Nguyen, A. Bouzerdoum, S. L. Phung. Pattern Recognition, Vukovar, Croatia: In-The, 2009, pp. 193-208.
  • [24] Z. H. Kilimci and S. İlhan Omurca, “Extended Feature Spaces Based Classifier Ensembles for Sentiment Analysis of Short Texts”, Inf Technol Control, vol. 47, no. 3, pp. 457-470, 2018.

Using Machine Learning Approaches for Prediction of the Types of Asthmatic Allergy across the Turkey

Year 2019, Volume: 2 Issue: 2, 8 - 12, 28.12.2019

Sevinç İlhan Omurca Ekin Ekinci Bengisu Çakmak Selim Gizem Özkan

Abstract

Nowadays, allergy is thought to be an important cause of frequent occurrence of diseases in the society we live in. Hence, finding out relation between patient characteristic variables such as age, sex and type of allergic diseases such as asthma, allergic rhinitis, food allergy, allergic dermatitis and so on is the main objective among allergy researchers. In this study, we propose to design an intelligent diagnostic assistant for prediction of the type of an allergic disease across Turkey automatically by using well-known machine learning algorithms such as Decision Tree, Logistic Regression, Support Vector Machines (SVM), K Nearest Neighbor (kNN) and ensemble classifiers. In experiments, an allergic diseases dataset, which is taken from Kocaeli University Research and Application Hospital, is utilized. As a result, in detecting 18 different allergy diagnoses, the maximum accuracy rate of 77% is achieved with majority voting.

Keywords

Allergy Classification Algorithms Ensemble Classifiers Machine Learning.

References

  • [1] C. Lagor, W. P. Lord, N. W. Chbat, J. D. Schaffer, and T. Wendler, "Advances in Healthcare Technology." Netherlands: Springer, 2006, ch. 22.
  • [2] R. Pawankari, "Allergic diseases and asthma: a global public health concern and a call to action." World Allergy Organ J, vol. 7, pp. 1-3, May 2014.
  • [3] A. Mari, E. Scala, P. Palazzo, S. Ridolfi, D. Zennaro, and G. Carabella, "Bioinformatics applied to allergy: Allergen databases, from collecting sequence information to data integration. The Allergome platform as a model." Cell Immunol., vol. 244, no. 2, pp. 97-100, Dec. 2006.
  • [4] G. Devereux, "The increase in the prevalence of asthma and allergy: food for thought." Nat Rev Immunol., vol. 6, no. 11, pp. 869-874, Nov. 2006.
  • [5] K. Kadam, S. Sawant, V. K. Jayaraman, and U. Kulkarni-Kale, "Bioinformatics–Updated Features and Applications." London, UK: IntechOpen, 2016, ch. 4.
  • [6] A. Zorzet, M. Gustafsson, and U. Hammerling, "Prediction of Food Protein Allergenicity: A Bio-informatic Learning Systems Approach." In Silico Biol., vol. 2, no. 4, pp. 525-534, 2002.
  • [7] D. Soeria-Atmadja, A. Zorzet, M. G. Gustafsson, and U. Hammerling, "Statistical Evaluation of Local Alignment Features Predicting Allergenicity Using Supervised Classification Algorithms." Int Arch Allergy Immunol., vol. 133, no. 2, pp. 101-112, Feb. 2004.
  • [8] I. Dimitrov, L. Naneva, I. Bangov, and I. Doytchinova, "Allergenicity prediction by artificial neural network." J Chemometr., vol. 28, no. 4, pp. 282-286, Jan. 2014.
  • [9] H. X. Dang, C. B. Lawrence, "Allerdictor: fast allergen prediction using text classification techniques." Bioinformatics, vol. 30, no. 8, pp. 1120-1128, Apr. 2014.
  • [10] H. F. Ng, H. M. Fathoni, and I. C. Chen, "Prediction of allergy symptoms among children in Taiwan using data mining," 2009.
  • [11] G. K. Zewdie, D. J. Lary, E. Levetin, and G. F. Garuma, "Applying Deep Neural Networks and Ensemble Machine Learning Methods to Forecast Airborne Ambrosia Pollen." Int J Environ Res Public Health., vol. 16, no. 11, Jun. 2019.
  • [12] S. Fontenella, C. Frainay, C. S. Murray, A. Smpson, and A. Custovic, "Machine learning to identify pairwise interactions between specific IgE antibodies and their association with asthma: A cross-sectional analysis within a population-based birth cohort." PLoS Med., vol. 15, no. 11, pp. 1-22, Nov. 2018. [Online]. Available: https://doi.org/10.1371/journal.pmed.1002691
  • [13] K. Lee, A. Agrawal, and A. Choudhary, "Mining Social Media Streams to Improve Public Health Allergy Surveillance." in Proc. 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, Paris, 2015, pp. 815–822.
  • [14] J. J. Christopher, H. K. Nehemiah, and A. Kannan, "A clinical decision support system for the diagnosis of Allergic Rhinitis based on intradermal skin tests." Comput Biol Med., vol. 65, pp. 76-84, Oct. 2015.
  • [15] J. R. Quinlan, "Induction of decision trees." Mach Learn., vol. 1, no. 1, pp. 81-106, Mar. 1986.
  • [16] M. S. Chen, J. Han, and P. S. Yu, "Data Mining: An Overview from a Database Perspective." IEEE T Knowl Data En., vol. 8, no. 6, pp. 866-883, Dec. 1996.
  • [17] H. Zhuang, Y. Ni, and S. Kokot, "Combining HPLC–DAD and ICP-MS data for improved analysis of complex samples: Classification of the root samples from Cortex." Chemometr Intell Lab., vol. 135, pp. 183-191, July 2014.
  • [18] C. J. C. Burges, "A Tutorial on Support Vector Machines for Pattern Recognition." Data Min Knowl Disc., vol. 2, no. 2, pp. 121-167, June 1998.
  • [19] L. Breiman, "Random Forests." Mach Learn., vol. 45, no. 1, pp. 5-32, Oct. 2001.
  • [20] E. Ekinci, S. İlhan Omurca, and N. Acun, "A Comparative Study on Machine Learning Techniques Using Titanic Dataset." in Proc. 7th International Conference on Advanced Technologies, Antalya, 2018, pp. 411-416.
  • [21] P. Geurts, D. Ernst, and L. Wehenkel, "Extremely randomized trees." Mach Learn., vol. 63, no. 1, pp. 3-42, Apr. 2006.
  • [22] Z. H. Kilimci and S. Omurca, "Enhancement of the Heuristic Optimization Based Extended Space Forests with Classifier Ensembles." Int Arab J Inf Techn., vol. 17, no. 2, Mar. 2020.
  • [23] H. G. Nguyen, A. Bouzerdoum, S. L. Phung. Pattern Recognition, Vukovar, Croatia: In-The, 2009, pp. 193-208.
  • [24] Z. H. Kilimci and S. İlhan Omurca, “Extended Feature Spaces Based Classifier Ensembles for Sentiment Analysis of Short Texts”, Inf Technol Control, vol. 47, no. 3, pp. 457-470, 2018.
There are 24 citations in total.

Details

Primary Language English
Subjects Artificial Life and Complex Adaptive Systems
Journal Section Research Article
Authors

Sevinç İlhan Omurca

Ekin Ekinci

Bengisu Çakmak This is me

Selim Gizem Özkan This is me

Publication Date December 28, 2019
Published in Issue Year 2019 Volume: 2 Issue: 2

Cite

IEEE S. İlhan Omurca, E. Ekinci, B. Çakmak, and S. G. Özkan, “Using Machine Learning Approaches for Prediction of the Types of Asthmatic Allergy across the Turkey”, International Journal of Data Science and Applications, vol. 2, no. 2, pp. 8–12, 2019.
 Download Cover Image

AI Research and Application Center, Sakarya University of Applied Sciences, Sakarya, Türkiye.