Assessment of Clinical Variables Importance with the Use of Neural Networks by the Example of Thyroid Blood Test Parameters
Screening blood tests for thyrometabolic status determination are difficult in interpretation because of many factors like age, sex or measurement method that influence their proper interpretation. To solve this problem machine learning techniques, like artificial neural networks (ANN) can be applied, but their application is very common belittled due to their very little explanatory insight to the relation between input parameters (e.g. test results) and model of the disease. In contrast to previous studies concerning application of neural networks in thyroid disease diagnosis, in this study the authors decided to focus on extraction of reliable dataset (with preserved proportion of diseased and health cases) and quantification of input parameters importance in neural network decisive process. The importance of the variables considered as the most significant in hypothyroidism detection was estimated based on two independent methods: connection weights method (according to the Garson’s algorithm) and sensitivity analysis. The results show, that the most important factors in hypothyroidism detection are TSH, TT4, FTI and age, and the rejection of other analyzed in this study parameters (sex, T3, T4U) does not influence significantly the performance of the neural network model and its predictive power.
KeywordsHypothyroidism Neural network analysis Parameter importance
This study was conducted with the use of database from UCI Machine Learning Repository donated by Garavan Institute in Sydney, Australia .
Presented research results were funded with the grant 02/21/DSMK/3529 and 02/21/DSPB/3513 allocated by the Ministry of Science and Higher Education in Poland.
- 1.Franklyn, J., Shephard, M.: Evaluation of thyroid function in health and disease. In: De Groot, L., Chrousos, G., Dungan, K., et al. (eds.) Endotext, South Dartmouth (MA) (2000)Google Scholar
- 2.Pellitteri, P.K., Ing, S., Jameson, B.: Disorders of the thyroid gland. In: Flint, P., Haughey, B.H., Lund, V., et al. (eds.) Cummings Otolaryngology, 6th edn, pp. 1884.e3–1900.e3. Elsevier Inc. (2015)Google Scholar
- 4.Tadeusiewicz, R., Korbicz, J., Rutkowski, L., Duch, W.: Sieci neuronowe w inzynierii biomedycznej. Tom 9. ang. Neural Networks in Biomedical Engineering, vol. 9. Akademicka Oficyna Wydawnicza EXIT, Warszawa (2013)Google Scholar
- 5.Michałowska, M., Walczak, T., Grabski, J.K., Grygorowicz, M.: Artificial neural networks in knee injury risk evaluation among professional football players. In: AIP Conference Proceedings, Lublin, p. 70002 (2018)Google Scholar
- 6.Walczak, T., Grabski, J., Grajewska, M., Michałowska, M.: Application of artificial neural networks in man’s gait recognition. In: Advances in Mechanics: Theoretical, Computational and Interdisciplinary Issues, pp. 591–594. CRC Press (2016)Google Scholar
- 7.Grabski, J.K., Walczak, T., Michałowska, M., Cieslak, M.: Gender recognition using artificial neural networks and data coming from force plates. In: Gzik, et al., M. (eds.) Innovations in Biomedical Engineering, IBE 2017, Advances in Intelligent Systems and Computing, vol. 623, pp. 53–60. Springer, Cham (2018)Google Scholar
- 10.Ozyilmz, L., Yildirim, T.: Diagnosis of thyroid disease using artificial neural network methods. In: Proceedings of the 9th International Conference Neural Information Process, vol. 4, pp. 2033–2036 (2002)Google Scholar
- 11.Dua, D., Karra Taniskidou, E.: UCI Machine Learning Repository. University of California, School of Information and Computer Science, Irvine (2017). http://archive.ics.uci.edu/ml. Accessed 30 Mar 2018