CLASSIFICATION AND FEATURE SELECTION IN MEDICAL DATA PREPROCESSING

  • Akhram Khasanovich Nishanov Information Technologies Software Department, Tashkent University of Information Technologies named after Muhammad al-
  • Gulomjon Primovich Djurayev Tashkent University of Information Technologies named after Muhammad al-Khwarizmi
  • Malika Akhramovna Khasanova Tashkent Medical Academy http://orcid.org/0000-0002-3128-9367
Keywords: Fishers Criterion, Feature Selection, Classification, Algorithms for an estimate calculation, Preprocessing of medical data

Abstract

In this article, the issues such as medical data preprocessing, reclassification of training sets and determining the importance of classes, forming reference tables, selecting a set of informative features that differentiate between class objects, formed by medical professionals, were solved based on Fisher criterion using algorithms for an estimate calculation. 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 estimate calculation as well as software programs for them were developed. As a result of 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 of data preprocessing, three sets consisting of 131, 115, and 40 objects, respectively, in the three classes were used to form a reference table.

Downloads

Download data is not yet available.

Author Biographies

Akhram Khasanovich Nishanov, Information Technologies Software Department, Tashkent University of Information Technologies named after Muhammad al-
Information Technologies Software Department,
Gulomjon Primovich Djurayev, Tashkent University of Information Technologies named after Muhammad al-Khwarizmi
Information Technologies Center at
Malika Akhramovna Khasanova, Tashkent Medical Academy
Department of Faculty and Hospital Therapy No.2

References

. Yu.I.Zhuravlev, “Selected scientific worksâ€, – M: “Magistr†Publishing house, 420 p., 1998.

. L.Yu, H.Liu. “Efficient Feature Selection via Analysis of Relevance and Redundancyâ€. – J. Mach. Learn. Res., Vol. 5, pp. 1205-1224, No Oct. 2004.

. L.Yu, “Toward Integrating Feature Selection Algorithm for Classification and Clusteringâ€, 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 Eliminationâ€, 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 pathologiesâ€, 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 systemâ€, Meditsinskaia tekhnika, (4) 16-18, 1999.

. F.Xiang, W.Lina, “Feature Selection Based on Fisher Criterion and Sequential Forward Selection for Intrusion Detectionâ€, 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 recognitionâ€, 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 Detectionsâ€, 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 Detectionâ€, Computer Science, 39(7), 96–100, 2012.

. K.Koushal, S.B.Jaspreet, “Network Intrusion Detection with Feature Selection Techniques using Machine-Learning Algorithmsâ€, 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 dataâ€, 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 Featuresâ€, International Journal of Innovative Technology and Exploring Engineering, 8(6), 2019.

. M.Sulaiman, J.Labadin, “Feature Selection Based on Mutual Informationâ€, 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 Bioinformaticsâ€, 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 and M.Aramudhan, “Feature Selection Based on Information Theory for Pattern Classificationâ€, in 2014 International Conference on Control, Instrumentation, Communication and Computational Technologies, doi: 10.1109/ICCICCT.2014.6993149, ICCICCT 2014.

. V. V.Mokshin, et al. “Parallel Genetic Algorithm of Feature Selection for Complex System Analysisâ€, 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 Methodsâ€, Artificial Intelligence Review. doi: 10.1007/s10462-019-09682-y, 2019.

. R.J.Urbanowicz et al. “Benchmarking Relief-Based Feature Selection Methods for Bioinformatics Data Miningâ€, Journal of Biomedical Informatics, 85. doi: 10.1016/j.jbi.2018.07.015, 2018.

. J.Cai et al. “Feature Selection in Machine Learning: A New Perspectiveâ€, Neurocomputing, 300. doi: 10.1016/j.neucom.2017.11.077, 2018.

. M.Ghaemi and M. R. Feizi-Derakhshi, “Feature Selection Using Forest Optimization Algorithmâ€, Pattern Recognition. Elsevier Ltd, 60, pp. 121–129. doi: 10.1016/j.patcog.2016.05.012, 2016.

Published
2020-07-02
How to Cite
Nishanov, A. K., Djurayev, G. P., & Khasanova, M. A. (2020). CLASSIFICATION AND FEATURE SELECTION IN MEDICAL DATA PREPROCESSING. COMPUSOFT: An International Journal of Advanced Computer Technology, 9(6), 3725-3732. Retrieved from https://ijact.in/index.php/ijact/article/view/1166