Classification and feature selection in medical data preprocessing
Keywords:
Fisher criterion, feature selection, classification, algorithms for an estimate calculation, medical data preprocessingAbstract
In this article, the issues such as medical data preprocessing, reclassification of training sets and determining the importance of classes, forming reference tables were solved. As a result of data preprocessing, three objects were formed: 1) Ischemic heart disease. Unstable angina pectoris; 2) Ischemic heart disease. Acute myocardial infarction; 3) Ischemic heart disease. Arrhythmic form. Further, the issues such as classifying, selecting a set of informative features that differentiate between class objects were solved using Fisher criterion and algorithms for an estimated calculation as well as software programs for them were developed. As a result data preprocessing reference classes were formed. Objects that had fallen outside of their class during the formation process were excluded from the training set. A classification and a set of informative features were selected using established classes. Initially, three class objects each containing 62 features were provided by medical professionals and as a result data preprocessing three sets consisting of 131, 115, and 40 objects respectively in three classes were used to form a reference table.
References
. Yu.I.Zhuravlev,“Selected scientific works”, –M: “Magistr” Publishing house, 420 p., 1998.
. L.Yu, H.Liu. “Efficient Feature Selection via Analysis of Relevance and Redundancy”. – J. Mach. Learn. Res., Vol. 5, pp. 1205-1224, No
Oct.2004.
. L.Yu, “Toward Integrating Feature Selection Algorithm for Classification and Clustering”, IEEE Transaction on Knowledge and Data Engineering 17(4), 491-502.
. K.Yan, D. Zhang, “Feature Selection and Analysis on Correlated Gas Sensor Data with Recursive Feature Elimination”, Sensors Actuators, B Chem., Vol. 212, pp. 353-363, Jun 2015.
. A.Kh. Nishanov, Kh.A. Turakulov, Kh.V.Turakhanov, “A decision rule for identification of eye pathologies”, Biomedical Engineering, 33(4) 178-179, 1999.
. A. Kh. Nishanov, Kh. A. Turakulov, Kh.V.Turakhanov, “A decisive rule in classifying diseases of the visual system”, Meditsinskaia tekhnika, (4) 16-18, 1999.
. F.Xiang, W.Lina,“Feature Selection Based on Fisher Criterion and Sequential Forward Selection for Intrusion Detection”, Revista de la Facultad de Ingeniería U.C.V., Vol. 32, N° 1, pp.498-503, 2017.
. S.Linhui, F.Sheng and F.Wang, “Decision tree SVM model with Fisher feature selection for speech emotion recognition”, Sun et al. EURASIP Journal on Audio, Speech, and Music Processing https://doi.org/10.1186/s13636-018-0145-5, 2019.
. X.Yan, M.Tan, Y.Yan, M.Lü, “Research on Hidden Markov Model-Based And Neural Network-Based Intrusion Detections”, Computer Applications and Software, 29(2),294–297, 2012.
. X.Jing, H. Wang, K. Nie, Z.Luo, “Feature Selection Algorithm Based on IMGA and MKSVM to Intrusion Detection”, Computer Science, 39(7), 96–100, 2012.
. K.Koushal, S.B. Jaspreet, “Network Intrusion Detection with Feature Selection Techniques using Machine-Learning Algorithms”, International Journal of Computer Applications (0975 – 8887) Volume 150 – No.12, 1-13, September-2016.
. A.Kh. Nishanov, G.P. Djurayev, M.A. Khasanova,“Improved algorithms for calculating evaluations in processing medical data”, COMPUSOFT: An International Journal of Advanced Computer Technology,India, Vol 8, No 6 - 2019.
. M. Kamilov, A.Nishanov, R.Beglerbekov, “Modified Stages of Algorithms for Computing Estimates in the Space of Informative Features”, International Journal of Innovative Technology and Exploring Engineering, 8(6), 2019.
. M.Sulaiman, J.Labadin, “Feature Selection Based on Mutual Information”, Publisher: Institute of Electrical and Electronics Engineers
(IEEE), pp: 1-6, 2015.
. D.Xuelian, L.Yuqing, W.Jian, Z.Jilian, “Feature Selection for Text Classification: A review” Multimedia Tools and Applications, 1007/s11042-018-6083-5, 2019.
. S.Ma and J.Huang, “Penalized Feature Selection and Classification in Bioinformatics”, Briefings in Bioinformatics. doi: 10.1093/bib/bbn027, 2008.
. L.Sun, , S.Fu and F.Wang, 2019) „Decision Tree SVM Model with Fisher Feature Selection for Speech Emotion Recognition‟, Eurasip Journal on Audio, Speech, and Music Processing, doi: 10.1186/s13636-018-0145-5, (1)2019.
. R. S. B.Krishna andM.Aramudhan, “Feature Selection Based on Information Theory for Pattern Classification”, in 2014 International
Conference on Control, Instrumentation, Communication and Computational Technologies, doi: 10.1109/ICCICCT.2014.6993149, ICCICCT
. V. V.Mokshin, et al. “Parallel Genetic Algorithm of Feature Selection for Complex System Analysis”, in Journal of Physics: Conference Series. doi: 10.1088/1742-6596/1096/1/012089, 2018.
. S.Solorio-Fernández, J.A.Carrasco-Ochoa and J.F.Martínez-Trinidad, “A Review of Unsupervised Feature Selection Methods”, Artificial Intelligence Review. doi: 10.1007/s10462-019-09682-y, 2019.
. R.J.Urbanowiczet al.“Benchmarking ReliefBased Feature Selection Methods for Bioinformatics Data Mining”, Journal of Biomedical Informatics, 85.doi: 10.1016/j.jbi.2018.07.015, 2018.
. J.Caiet al.“Feature Selection in Machine Learning: A New Perspective”, Neurocomputing, 300. doi: 10.1016/j.neucom.2017.11.077, 2018.
. M.Ghaemiand M. R. Feizi-Derakhshi, “Feature Selection Using Forest Optimization Algorithm”, Pattern Recognition. Elsevier Ltd,
, pp. 121–129. doi: 10.1016/j.patcog.2016.05.012, 2016.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2020 COMPUSOFT: An International Journal of Advanced Computer Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.
©2023. COMPUSOFT: AN INTERNATIONAL OF ADVANCED COMPUTER TECHNOLOGY by COMPUSOFT PUBLICATION is licensed under a Creative Commons Attribution 4.0 International License. Based on a work at COMPUSOFT: AN INTERNATIONAL OF ADVANCED COMPUTER TECHNOLOGY. Permissions beyond the scope of this license may be available at Creative Commons Attribution 4.0 International Public License.
