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Covid-19 Hastalarının Ölüm Oranlarının ve Yüksek Ölüm Riskine Sahip Hastaların Belirlenmesi için Temel Bileşen Analizinin Kullanılması

Year 2022, Volume: 5 Issue: 2, 127 - 136, 21.09.2022

Ebru Efeoğlu

https://doi.org/10.38016/jista.1082310

Abstract

Covid-19 virüsü 2019 yılında ortaya çıktı ve kısa bir sürede tüm dünyaya yayıldı. Milyonlarca insanın enfekte olmasına ve yüz binlerce insanın ölümüne neden oldu. Her geçen gün vaka sayısı artmakta ve virüsün yeni varyantlar meydana gelmektedir. Bu hastalığa sahip kişileri tespit etmek için Polimeraz Zincir Reaksiyonu (PCR) testleri uygulanmaktadır. Hastalığı tespit edilen kişilerin durumlarının incelenmesi yoğun bakım ve ölüm oranlarının önceden tespiti oldukça önemlidir. Bu çalışmada Covid-19 hastalarından ölüm oranlarının tespitinde özellik çıkarımı yöntemi olarak Temel Bileşen Analizi (PCA) kullanılmış ve yöntemin başarılı sonuçları en popüler makine öğrenmesi teknikleri ile gösterilmiştir. Çalışmada kullanılan makine öğrenmesi teknikleri K-En Yakın Komşu (KNN), Doğrusal Ayrımcılık Analizi (LDA), Extra Ağaçlar, Random Tree, Rep Tree ve Naive Bayes algoritmalarıdır. Bu tekniklerin performans değerlendirmesinde Doğruluk, Kesinlik, Duyarlılık, Rms, F-skoru değerleri hesaplanmıştır. Ayrıca ROC Eğrileri ve Karışıklık matrisleri incelenerek sonuçlar karşılaştırılmıştır. Sonuç olarak, en iyi performansın Temel bileşenler analizi uygulandıktan sonra Doğrusal Ayrım Analizi (PCA+LDA) kullanımı ile elde edildiği görülmüştür. PCA+LDA uygulaması ile %96,39 Doğruluk oranı elde edilmiştir. Makalede ayrıca özellik çıkarımının kullanılmasıyla Covid-19 virüsünden Zatürre, Şeker, KOAH ve Astım hastalarının, hamile, yaşlı ve entrube insanların daha çok etkilendiği ve ölüm riskinin daha yüksek olduğu ortaya çıkmıştır. Virüsün varyantlarının ölümcüllüğünün incelenmesi, riskli hastaların tedavisi, ölüm riski bulunan hastaların izolasyonu için gereken önlemlerin alınması ve hastane kapasite planlamasının iyileştirilmesi açısından bu çalışma önem arz etmektedir.

Keywords

Covid-19 Performans Analizi Sınıflandırma Algoritmaları Temel Bileşen Analizi

References

  • Abdi, H., & Williams, L. J. 2010. Principal component analysis. Computational Statistics.
  • Akhtar, A., Akhtar, S., Bakhtawar, B., Kashif, A. A., Aziz, N., & Javeid, M. S. 2021. COVID-19 Detection from CBC using Machine Learning Techniques. International Journal of Technology, Innovation and Management (IJTIM), 1(2), 65-78.
  • Albahri, A. S., Hamid, R. A., Alwan, J. K., Al-Qays, Z., Zaidan, A., Zaidan, B., . . . Almahdi, E. 2020. Role of biological data mining and machine learning techniques in detecting and diagnosing the novel coronavirus (COVID-19): a systematic review. Journal of medical systems, 44, 1-11.
  • Amasyali, M. F., & Ersoy, O. 2009. Evaluation of regression ensembles on drug design datasets.
  • Bello-Chavolla, O. Y., Bahena-López, J. P., Antonio-Villa, N. E., Vargas-Vázquez, A., González-Díaz, A., Márquez-Salinas, A., . . . Aguilar-Salinas, C. A. (2020). Predicting mortality due to SARS-CoV-2: a mechanistic score relating obesity and diabetes to COVID-19 outcomes in Mexico. The Journal of Clinical Endocrinology & Metabolism, 105(8), 2752-2761.
  • Bermejo, P., Gámez, J. A., & Puerta, J. M. 2011. Improving the performance of Naive Bayes multinomial in e-mail foldering by introducing distribution-based balance of datasets. Expert Systems with Applications, 38(3), 2072-2080.
  • Breiman L., 2001, Random forests,machine learning, 2001 Kluwer Academic Publishers, 45(1), 5-32. COVID-19 Mexico Patient Health Dataset. (2020, 05 19). Retrieved from Kaggle.com: https://www.kaggle.com/datasets/riteshahlawat/covid19-mexico-patient-health-dataset
  • Chinazzi, M., Davis, J. T., Ajelli, M., Gioannini, C., Litvinova, M., Merler, S., . . . Sun, K. (2020). The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak. Science, 368(6489), 395-400.
  • de León, U. A.-P., Pérez, Á. G., & Avila-Vales, E. (2020). An SEIARD epidemic model for COVID-19 in Mexico: mathematical analysis and state-level forecast. Chaos, Solitons & Fractals, 140, 110165.
  • Drew, D. A., Nguyen, L. H., Steves, C. J., Menni, C., Freydin, M., Varsavsky, T., . . . Wolf, J. (2020). Rapid implementation of mobile technology for real-time epidemiology of COVID-19. Science, 368(6497), 1362-1367.
  • Escobedo-de la Peña, J., Rascón-Pacheco, R. A., de Jesús Ascencio-Montiel, I., González-Figueroa, E., Fernández-Gárate, J. E., Medina-Gómez, O. S., . . . Borja-Aburto, V. H. (2021). Hypertension, diabetes and obesity, major risk factors for death in patients with COVID-19 in Mexico. Archives of medical research, 52(4), 443-449.
  • Freund, Y., & Mason, L. 1999. The alternating decision tree learning algorithm. Paper presented at the icml.
  • Gansevoort, R. T., & Hilbrands, L. B. (2020). CKD is a key risk factor for COVID-19 mortality. Nature Reviews Nephrology, 16(12), 705-706.
  • Guan, X., Zhang, B., Fu, M., Li, M., Yuan, X., Zhu, Y., . . . Lu, Y. 2021. Clinical and inflammatory features-based machine learning model for fatal risk prediction of hospitalized COVID-19 patients: results from a retrospective cohort study. Annals of Medicine, 53(1), 257-266.
  • Jagodnik, K. M., Ray, F., Giorgi, F. M., & Lachmann, A. 2020. Correcting under-reported COVID-19 case numbers: estimating the true scale of the pandemic. medRxiv.
  • Kassania, S. H., Kassanib, P. H., Wesolowskic, M. J., Schneidera, K. A., & Detersa, R. 2021. Automatic detection of coronavirus disease (COVID-19) in X-ray and CT images: a machine learning based approach. Biocybernetics and Biomedical Engineering, 41(3), 867-879.
  • Kira, K., & Rendell, L. A. (1992). A practical approach to feature selection. In Machine learning proceedings 1992 (pp. 249-256): Elsevier.
  • Kohavi, R., & John, G. H. (1997). Wrappers for feature subset selection. Artificial intelligence, 97(1-2), 273-324.
  • Levin, A. T., Cochran, K., & Walsh, S. 2020. Assessing the age specificity of infection fatality rates for COVID-19: Meta-analysis & public policy implications. NBER Working Paper(w27597).
  • Li, B., Yu, S., & Lu, Q. 2003. An improved k-nearest neighbor algorithm for text categorization. arXiv preprint cs/0306099.
  • Li, J., Zhang, S., Lu, Y., & Yan, J. 2008. Real-time P2P traffic identification. Paper presented at the IEEE GLOBECOM 2008-2008 IEEE Global Telecommunications Conference.
  • Li, M., Zhang, Z., Cao, W., Liu, Y., Du, B., Chen, C., . . . Chen, C. 2021. Identifying novel factors associated with COVID-19 transmission and fatality using the machine learning approach. Science of the Total Environment, 764, 142810.
  • Maglaveras, N., Stamkopoulos, T., Diamantaras, K., Pappas, C., & Strintzis, M. 1998. ECG pattern recognition and classification using non-linear transformations and neural networks: A review. International journal of medical informatics, 52(1-3), 191-208.
  • Manski, C. F., & Molinari, F. 2021. Estimating the COVID-19 infection rate: Anatomy of an inference problem. Journal of Econometrics, 220(1), 181-192.
  • Muhammad, L., Algehyne, E. A., Usman, S. S., Ahmad, A., Chakraborty, C., & Mohammed, I. A. (2021). Supervised machine learning models for prediction of COVID-19 infection using epidemiology dataset. SN computer science, 2(1), 1-13.
  • Nemati, M., Ansary, J., & Nemati, N. (2020). Machine-learning approaches in COVID-19 survival analysis and discharge-time likelihood prediction using clinical data. Patterns, 1(5), 100074.
  • Novaković, J., Strbac, P., & Bulatović, D. (2011). Toward optimal feature selection using ranking methods and classification algorithms. Yugoslav Journal of operations research, 21(1), 119-135.
  • Parra-Bracamonte, G. M., Lopez-Villalobos, N., & Parra-Bracamonte, F. E. (2020). Clinical characteristics and risk factors for mortality of patients with COVID-19 in a large data set from Mexico. Annals of epidemiology, 52, 93-98. e92.
  • Quiroz-Juárez, M. A., Torres-Gómez, A., Hoyo-Ulloa, I., León-Montiel, R. d. J., & U’Ren, A. B. (2021). Identification of high-risk COVID-19 patients using machine learning. Plos one, 16(9), e0257234.
  • Quinlan, J. R. 1999. Simplifying decision trees. International Journal of Human-Computer Studies, 51(2), 497-510.
  • Rahmandad, H., Lim, T. Y., & Sterman, J. 2020. Estimating COVID-19 under-reporting across 86 nations: implications for projections and control. medRxiv.
  • Ríos-Silva, M., Murillo-Zamora, E., Mendoza-Cano, O., Trujillo, X., & Huerta, M. (2020). COVID-19 mortality among pregnant women in Mexico: a retrospective cohort study. Journal of Global Health, 10(2).
  • Singh, J., Green, M. B., Lindblom, S., Reif, M. S., Thakkar, N. P., & Papali, A. (2021). Telecritical care clinical and operational strategies in response to COVID-19. Telemedicine and e-Health, 27(3), 261-268.
  • Stella, L., Martínez, A. P., Bauso, D., & Colaneri, P. 2020. The role of asymptomatic individuals in the Covid-19 pandemic via complex networks. arXiv preprint arXiv:2009.03649.
  • Ünsal, A., Bileşenler, Ö., _Faktür, M., & Mali, D. A. Y. I. Ş. 1996. Başarılarının Analizi. In: Ankara.
  • Velavan, T. P., & Meyer, C. G. 2020. The COVID‐19 epidemic. Tropical medicine & international health, 25(3), 278.
  • Xia, S., Xiong, Z., Luo, Y., Dong, L., & Zhang, G. 2015. Location difference of multiple distances-based k-nearest neighbors’ algorithm. Knowledge-Based Systems, 90, 99-110.
  • Yang, S., Cao, P., Du, P., Wu, Z., Zhuang, Z., Yang, L., . . . Wang, X. 2020. Early estimation of the case fatality rate of COVID-19 in mainland China: a data-driven analysis. Annals of translational medicine, 8(4).
  • Yavuz, Ü., & Dudak, M. N. 2020. Classification of covid-19 dataset with some machine learning methods. journal of amasya university the institute of sciences and technology, 1(1), 30-37.
  • Zawiah, M., Al-Ashwal, F. Y., Saeed, R. M., Kubas, M., Saeed, S., Khan, A. H., . . . Abduljabbar, R. (2020). Assessment of healthcare system capabilities and preparedness in Yemen to Confront the novel coronavirus 2019 (COVID-19) outbreak: a perspective of healthcare workers. Frontiers in public health, 419.
  • Zens, M., Brammertz, A., Herpich, J., Südkamp, N., & Hinterseer, M. (2020). App-based tracking of self-reported COVID-19 symptoms: analysis of questionnaire data. Journal of medical Internet research, 22(9), e21956.
  • Zhang, Y., Ding, L., & Wang, Y. 2011. Research and design of ID3 algorithm rules-based anti-spam email filtering. Paper presented at the 2011 IEEE 2nd International Conference on Software Engineering and Service Science.

Using Principal Component Analysis to Identify Mortality Rates of Covid-19 Patients and Patients at High Risk of Death

Year 2022, Volume: 5 Issue: 2, 127 - 136, 21.09.2022

Ebru Efeoğlu

https://doi.org/10.38016/jista.1082310

Abstract

The Covid-19 virus emerged in 2019 and spread all over the world in a short time. It caused millions of people to be infected and hundreds of thousands to die. The number of cases is increasing day by day and new variants of the virus are emerging. Polymerase Chain Reaction (PCR) tests are used to detect people with this disease. It is very important to examine the conditions of the people with the disease and to determine the intensive care and mortality rates in advance. In this study, Principal Component Analysis (PCA) was used as a feature extraction method to determine mortality rates from Covid-19 patients, and the successful results of the method were demonstrated with the most popular machine learning techniques. Machine learning techniques used in the study are K-Nearest Neighbor (KNN), Linear Discrimination Analysis (LDA), Extra Trees, Random Tree, Rep Tree and Naive Bayes algorithms. In the performance evaluation of these techniques, Accuracy, Precision, Sensitivity, Rms, F-score values were calculated. In addition, ROC Curves and Confusion matrices were examined and the results were compared. As a result, it was seen that the best performance was obtained with the use of Linear Discrimination Analysis (PCA+LDA) after applying Principal component analysis. With the PCA+LDA application, an accuracy rate of 96.39% was obtained. In the article, it has also been revealed that Pneumonia, Diabetes, COPD and Asthma patients, Pregnant, Elderly and Intubated people are more affected and the risk of death is higher from the Covid- 19 virus by using feature extraction. This study is important in terms of examining the lethality of virus variants, taking the necessary precautions for the treatment of risky patients isolation of patients at risk of death, and improving hospital capacity planning.

Keywords

Covid-19 Performance Analysis Classifications Principal Component Analysis

References

  • Abdi, H., & Williams, L. J. 2010. Principal component analysis. Computational Statistics.
  • Akhtar, A., Akhtar, S., Bakhtawar, B., Kashif, A. A., Aziz, N., & Javeid, M. S. 2021. COVID-19 Detection from CBC using Machine Learning Techniques. International Journal of Technology, Innovation and Management (IJTIM), 1(2), 65-78.
  • Albahri, A. S., Hamid, R. A., Alwan, J. K., Al-Qays, Z., Zaidan, A., Zaidan, B., . . . Almahdi, E. 2020. Role of biological data mining and machine learning techniques in detecting and diagnosing the novel coronavirus (COVID-19): a systematic review. Journal of medical systems, 44, 1-11.
  • Amasyali, M. F., & Ersoy, O. 2009. Evaluation of regression ensembles on drug design datasets.
  • Bello-Chavolla, O. Y., Bahena-López, J. P., Antonio-Villa, N. E., Vargas-Vázquez, A., González-Díaz, A., Márquez-Salinas, A., . . . Aguilar-Salinas, C. A. (2020). Predicting mortality due to SARS-CoV-2: a mechanistic score relating obesity and diabetes to COVID-19 outcomes in Mexico. The Journal of Clinical Endocrinology & Metabolism, 105(8), 2752-2761.
  • Bermejo, P., Gámez, J. A., & Puerta, J. M. 2011. Improving the performance of Naive Bayes multinomial in e-mail foldering by introducing distribution-based balance of datasets. Expert Systems with Applications, 38(3), 2072-2080.
  • Breiman L., 2001, Random forests,machine learning, 2001 Kluwer Academic Publishers, 45(1), 5-32. COVID-19 Mexico Patient Health Dataset. (2020, 05 19). Retrieved from Kaggle.com: https://www.kaggle.com/datasets/riteshahlawat/covid19-mexico-patient-health-dataset
  • Chinazzi, M., Davis, J. T., Ajelli, M., Gioannini, C., Litvinova, M., Merler, S., . . . Sun, K. (2020). The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak. Science, 368(6489), 395-400.
  • de León, U. A.-P., Pérez, Á. G., & Avila-Vales, E. (2020). An SEIARD epidemic model for COVID-19 in Mexico: mathematical analysis and state-level forecast. Chaos, Solitons & Fractals, 140, 110165.
  • Drew, D. A., Nguyen, L. H., Steves, C. J., Menni, C., Freydin, M., Varsavsky, T., . . . Wolf, J. (2020). Rapid implementation of mobile technology for real-time epidemiology of COVID-19. Science, 368(6497), 1362-1367.
  • Escobedo-de la Peña, J., Rascón-Pacheco, R. A., de Jesús Ascencio-Montiel, I., González-Figueroa, E., Fernández-Gárate, J. E., Medina-Gómez, O. S., . . . Borja-Aburto, V. H. (2021). Hypertension, diabetes and obesity, major risk factors for death in patients with COVID-19 in Mexico. Archives of medical research, 52(4), 443-449.
  • Freund, Y., & Mason, L. 1999. The alternating decision tree learning algorithm. Paper presented at the icml.
  • Gansevoort, R. T., & Hilbrands, L. B. (2020). CKD is a key risk factor for COVID-19 mortality. Nature Reviews Nephrology, 16(12), 705-706.
  • Guan, X., Zhang, B., Fu, M., Li, M., Yuan, X., Zhu, Y., . . . Lu, Y. 2021. Clinical and inflammatory features-based machine learning model for fatal risk prediction of hospitalized COVID-19 patients: results from a retrospective cohort study. Annals of Medicine, 53(1), 257-266.
  • Jagodnik, K. M., Ray, F., Giorgi, F. M., & Lachmann, A. 2020. Correcting under-reported COVID-19 case numbers: estimating the true scale of the pandemic. medRxiv.
  • Kassania, S. H., Kassanib, P. H., Wesolowskic, M. J., Schneidera, K. A., & Detersa, R. 2021. Automatic detection of coronavirus disease (COVID-19) in X-ray and CT images: a machine learning based approach. Biocybernetics and Biomedical Engineering, 41(3), 867-879.
  • Kira, K., & Rendell, L. A. (1992). A practical approach to feature selection. In Machine learning proceedings 1992 (pp. 249-256): Elsevier.
  • Kohavi, R., & John, G. H. (1997). Wrappers for feature subset selection. Artificial intelligence, 97(1-2), 273-324.
  • Levin, A. T., Cochran, K., & Walsh, S. 2020. Assessing the age specificity of infection fatality rates for COVID-19: Meta-analysis & public policy implications. NBER Working Paper(w27597).
  • Li, B., Yu, S., & Lu, Q. 2003. An improved k-nearest neighbor algorithm for text categorization. arXiv preprint cs/0306099.
  • Li, J., Zhang, S., Lu, Y., & Yan, J. 2008. Real-time P2P traffic identification. Paper presented at the IEEE GLOBECOM 2008-2008 IEEE Global Telecommunications Conference.
  • Li, M., Zhang, Z., Cao, W., Liu, Y., Du, B., Chen, C., . . . Chen, C. 2021. Identifying novel factors associated with COVID-19 transmission and fatality using the machine learning approach. Science of the Total Environment, 764, 142810.
  • Maglaveras, N., Stamkopoulos, T., Diamantaras, K., Pappas, C., & Strintzis, M. 1998. ECG pattern recognition and classification using non-linear transformations and neural networks: A review. International journal of medical informatics, 52(1-3), 191-208.
  • Manski, C. F., & Molinari, F. 2021. Estimating the COVID-19 infection rate: Anatomy of an inference problem. Journal of Econometrics, 220(1), 181-192.
  • Muhammad, L., Algehyne, E. A., Usman, S. S., Ahmad, A., Chakraborty, C., & Mohammed, I. A. (2021). Supervised machine learning models for prediction of COVID-19 infection using epidemiology dataset. SN computer science, 2(1), 1-13.
  • Nemati, M., Ansary, J., & Nemati, N. (2020). Machine-learning approaches in COVID-19 survival analysis and discharge-time likelihood prediction using clinical data. Patterns, 1(5), 100074.
  • Novaković, J., Strbac, P., & Bulatović, D. (2011). Toward optimal feature selection using ranking methods and classification algorithms. Yugoslav Journal of operations research, 21(1), 119-135.
  • Parra-Bracamonte, G. M., Lopez-Villalobos, N., & Parra-Bracamonte, F. E. (2020). Clinical characteristics and risk factors for mortality of patients with COVID-19 in a large data set from Mexico. Annals of epidemiology, 52, 93-98. e92.
  • Quiroz-Juárez, M. A., Torres-Gómez, A., Hoyo-Ulloa, I., León-Montiel, R. d. J., & U’Ren, A. B. (2021). Identification of high-risk COVID-19 patients using machine learning. Plos one, 16(9), e0257234.
  • Quinlan, J. R. 1999. Simplifying decision trees. International Journal of Human-Computer Studies, 51(2), 497-510.
  • Rahmandad, H., Lim, T. Y., & Sterman, J. 2020. Estimating COVID-19 under-reporting across 86 nations: implications for projections and control. medRxiv.
  • Ríos-Silva, M., Murillo-Zamora, E., Mendoza-Cano, O., Trujillo, X., & Huerta, M. (2020). COVID-19 mortality among pregnant women in Mexico: a retrospective cohort study. Journal of Global Health, 10(2).
  • Singh, J., Green, M. B., Lindblom, S., Reif, M. S., Thakkar, N. P., & Papali, A. (2021). Telecritical care clinical and operational strategies in response to COVID-19. Telemedicine and e-Health, 27(3), 261-268.
  • Stella, L., Martínez, A. P., Bauso, D., & Colaneri, P. 2020. The role of asymptomatic individuals in the Covid-19 pandemic via complex networks. arXiv preprint arXiv:2009.03649.
  • Ünsal, A., Bileşenler, Ö., _Faktür, M., & Mali, D. A. Y. I. Ş. 1996. Başarılarının Analizi. In: Ankara.
  • Velavan, T. P., & Meyer, C. G. 2020. The COVID‐19 epidemic. Tropical medicine & international health, 25(3), 278.
  • Xia, S., Xiong, Z., Luo, Y., Dong, L., & Zhang, G. 2015. Location difference of multiple distances-based k-nearest neighbors’ algorithm. Knowledge-Based Systems, 90, 99-110.
  • Yang, S., Cao, P., Du, P., Wu, Z., Zhuang, Z., Yang, L., . . . Wang, X. 2020. Early estimation of the case fatality rate of COVID-19 in mainland China: a data-driven analysis. Annals of translational medicine, 8(4).
  • Yavuz, Ü., & Dudak, M. N. 2020. Classification of covid-19 dataset with some machine learning methods. journal of amasya university the institute of sciences and technology, 1(1), 30-37.
  • Zawiah, M., Al-Ashwal, F. Y., Saeed, R. M., Kubas, M., Saeed, S., Khan, A. H., . . . Abduljabbar, R. (2020). Assessment of healthcare system capabilities and preparedness in Yemen to Confront the novel coronavirus 2019 (COVID-19) outbreak: a perspective of healthcare workers. Frontiers in public health, 419.
  • Zens, M., Brammertz, A., Herpich, J., Südkamp, N., & Hinterseer, M. (2020). App-based tracking of self-reported COVID-19 symptoms: analysis of questionnaire data. Journal of medical Internet research, 22(9), e21956.
  • Zhang, Y., Ding, L., & Wang, Y. 2011. Research and design of ID3 algorithm rules-based anti-spam email filtering. Paper presented at the 2011 IEEE 2nd International Conference on Software Engineering and Service Science.
There are 42 citations in total.

Details

Primary Language Turkish
Subjects Computer Software
Journal Section Research Articles
Authors

Ebru Efeoğlu 0000-0001-5444-6647

Early Pub Date June 14, 2022
Publication Date September 21, 2022
Submission Date March 3, 2022
Published in Issue Year 2022 Volume: 5 Issue: 2

Cite

APA Efeoğlu, E. (2022). Covid-19 Hastalarının Ölüm Oranlarının ve Yüksek Ölüm Riskine Sahip Hastaların Belirlenmesi için Temel Bileşen Analizinin Kullanılması. Journal of Intelligent Systems: Theory and Applications, 5(2), 127-136. https://doi.org/10.38016/jista.1082310
AMA Efeoğlu E. Covid-19 Hastalarının Ölüm Oranlarının ve Yüksek Ölüm Riskine Sahip Hastaların Belirlenmesi için Temel Bileşen Analizinin Kullanılması. JISTA. September 2022;5(2):127-136. doi:10.38016/jista.1082310
Chicago Efeoğlu, Ebru. “Covid-19 Hastalarının Ölüm Oranlarının Ve Yüksek Ölüm Riskine Sahip Hastaların Belirlenmesi için Temel Bileşen Analizinin Kullanılması”. Journal of Intelligent Systems: Theory and Applications 5, no. 2 (September 2022): 127-36. https://doi.org/10.38016/jista.1082310.
EndNote Efeoğlu E (September 1, 2022) Covid-19 Hastalarının Ölüm Oranlarının ve Yüksek Ölüm Riskine Sahip Hastaların Belirlenmesi için Temel Bileşen Analizinin Kullanılması. Journal of Intelligent Systems: Theory and Applications 5 2 127–136.
IEEE E. Efeoğlu, “Covid-19 Hastalarının Ölüm Oranlarının ve Yüksek Ölüm Riskine Sahip Hastaların Belirlenmesi için Temel Bileşen Analizinin Kullanılması”, JISTA, vol. 5, no. 2, pp. 127–136, 2022, doi: 10.38016/jista.1082310.
ISNAD Efeoğlu, Ebru. “Covid-19 Hastalarının Ölüm Oranlarının Ve Yüksek Ölüm Riskine Sahip Hastaların Belirlenmesi için Temel Bileşen Analizinin Kullanılması”. Journal of Intelligent Systems: Theory and Applications 5/2 (September 2022), 127-136. https://doi.org/10.38016/jista.1082310.
JAMA Efeoğlu E. Covid-19 Hastalarının Ölüm Oranlarının ve Yüksek Ölüm Riskine Sahip Hastaların Belirlenmesi için Temel Bileşen Analizinin Kullanılması. JISTA. 2022;5:127–136.
MLA Efeoğlu, Ebru. “Covid-19 Hastalarının Ölüm Oranlarının Ve Yüksek Ölüm Riskine Sahip Hastaların Belirlenmesi için Temel Bileşen Analizinin Kullanılması”. Journal of Intelligent Systems: Theory and Applications, vol. 5, no. 2, 2022, pp. 127-36, doi:10.38016/jista.1082310.
Vancouver Efeoğlu E. Covid-19 Hastalarının Ölüm Oranlarının ve Yüksek Ölüm Riskine Sahip Hastaların Belirlenmesi için Temel Bileşen Analizinin Kullanılması. JISTA. 2022;5(2):127-36.
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