Evaluating Machine Learning Models for Healthcare Services Efficiency
DOI:
https://doi.org/10.14571/brajets.v16.n4.1280-1289Abstract
The article is dedicated to the problem of assessing the quality of healthcare using machine learning algorithms. It is proposed to evaluate categorical term “healthcare quality” as discrete variable on the basis of data on treatment result, provided by one of the major hospitals in Russia. Preliminary analysis of both dependent and explanatory variables is provided. The statistics of results of treatment depending on the age and gender of patients are analyzed. The necessity of reducing the initial dataset is justified. The ways of correction the imbalance in dataset is proposed, such as SMOTE and Tomek Links On the basis of corrected data ordered logit and Random Forest models were designed. The comparative analysis of various models is presented. The drawbacks of each model are explained. The ordered logit model on balanced data allowed to determine factors, that have greatest positive impact on the treatment result as well as Random Forest model made the predictions as accurate as it is possible. On the basis of results obtained, the recommendations to improve the healthcare efficiency in the framework of particular hospital are made.References
Cardenas, C. A. (2023). Sarcomas:“A Comprehensive Review of Classification, Diagnosis, Treatment, and Psycho-social Aspects. Clin Oncol Case Rep 6, 6, 2-6.
Chawla N.V., Bowyer K.W., Hall L.O., W.P. (2002). Kegelmeyer SMOTE: Synthetic Minority Over-sampling Tech-nique. Journal of Artificial Intelligence Research, 16, 321–357.
Donoso, P. C., Pérez, M. P. S., Aguirre, C. C., Barbosa, A. O., Gómez, C. M. G., Jimenez, A. M., & Nodar, S. R. (2022). Angiosarcoma suprarrenal primario. Reporte de caso. Archivos de Patologia, 3(3), 96-103.
Farhud, D., & Mojahed, N. (2022). SARS-COV-2 Notable Mutations and Variants: A Review Article. Iranian Jour-nal of Public Health, 51(7), 1494.
Ferrer, N. R., Romero, M. B., Ochenduszko, S., Perpiñá, L. G., Malagón, S. P., Arbat, J. R., & Nodar, S. R. (2022). Solitary fibrous tumor of the thyroid. Report of a case with unusual clinical and morphological findings Ar-chivos de Patologia, 3(3), 104-109.
Hastie T., Tibshirani R., Friedman J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Pre-diction. Second Edition, Springer, New York
He H., Garcia E. A. (2009). Learning from Imbalanced Data. IEEE Transactions on Knowledge and Data Engi-neering, 21, 1263–1284. DOI: 10.1109/TKDE.2008.239.
Jamalpour, H., & Yaghoobi-Derab, J. (2022). A review of the philosophy of aesthetics and art based on theoretical and methodological considerations. Revista de Investigaciones Universidad del Quindío, 34(S2), 426-435.
James G., Witten D., Hastie T, Tibshirani R. (2017). An Introduction to Statistical Learning. Springer, New York.
Kavrin D.A., Subbotin S.A. (2018). Methods for quantitatively solving the problem of class imbalance. Radio Elec-tronics, Computer Science, Control, 1(44), 83-90.
King, G., & Zeng, L. (2001). Logistic Regression in Rare Events Data. Political Analysis, 9, 137 - 163
Shariati, A., Azaribeni, A., Hajighahramanzadeh, P., & Loghmani, Z. (2013). Liquid–liquid equilibria of systems containingsunflower oil, ethanol and water. APCBEE procedia, 5, 486-490.
Sherafatizangeneh, M., Farshadfar, C., Mojahed, N., Noorbakhsh, A., & Ardalan, N. (2022). Blockage of the Mono-amine Oxidase by a Natural Compound to Overcome Parkinson’s Disease via Computational Biology. Journal of Computational Biophysics and Chemistry, 21(3), 373-387.
Shunina Yu.S., Alekseeva V.A., Klyachkin V.N. (2015). Performance criteria for classifiers. Bulletin of Ulyanovsk State Technical University, 2(70), 67-70.
Tikhomirov, N. P., Tikhomirova, T. M. (2021). Methods of justification of effective demographic policy. In: Kitova, O., Dyakonova L. (eds.) Information Technologies and Mathematical Methods in Economics and Manage-ment (IT&MM-2020), Proceedings of the 10th International Scientific and Practical Conference named after A. I. Kitov, 10, 52–62. Plekhanov Russian University of Economic, Moscow.
Tikhomirova, T. M., Sukiasyan A. G. (2020). Econometrics and Modelling in Management. Plekhanov Russian University of Economic, Moscow.
Tikhomirova, T. M., Sukiasyan A. G. (2021). Comparative estimates of human potential taking into consideration the risks of social inequality. In: Kitova, O., Dyakonova L. (eds.) Information Technologies and Mathematical Methods in Economics and Management (IT&MM-2020), Proceedings of the 10th International Scientific and Practical Conference named after A. I. Kitov, 10, 63–76. Plekhanov Russian University of Economic, Mos-cow.
Tomek, I. (2010). Two modifications of CNN,” In Systems, Man, and Cybernetics. IEEE Transactions on, 6, 769-772.
Voronkova, O. Y., Volokhova, T. V., Lebedeva, E. S., Smirnova, A. V., & Tubalets, A. A. (2022). Priorities for the development of medicinal plant growing in a post-pandemic environment. Siberian Journal of Life Sciences and Agriculture, 14(1), 436-451. doi:10.12731/2658-6649-2022-14-1-436-451
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