Araştırma Makalesi
BibTex RIS Kaynak Göster

Yıl 2023, Cilt: 12 Sayı: 4, 1261 - 1274, 28.12.2023

Pınar Karadayı Ataş

https://doi.org/10.17798/bitlisfen.1376817

Öz

Kaynakça

  • [1] H. Singh, A. N. D. Meyer, and E. J. Thomas, ‘The frequency of diagnostic errors in outpatient care: estimations from three large observational studies involving US adult populations’, BMJ Qual. Saf., vol. 23, no. 9, pp. 727–731, Sep. 2014, doi: 10.1136/bmjqs-2013-002627.
  • [2] T. M. Ghazal, A. U. Rehman, M. Saleem, M. Ahmad, S. Ahmad, and F. Mehmood, ‘Intelligent Model to Predict Early Liver Disease using Machine Learning Technique’, presented at the 2022 International Conference on Business Analytics for Technology and Security (ICBATS), IEEE, 2022, pp. 1–5.
  • [3] M. L. Graber, ‘The incidence of diagnostic error in medicine’, BMJ Qual. Saf., vol. 22, no. Suppl 2, pp. ii21–ii27, Oct. 2013, doi: 10.1136/bmjqs-2012-001615.
  • [4] J. J. Deeks, P. M. Bossuyt, M. M. Leeflang, and Y. Takwoingi, Cochrane handbook for systematic reviews of diagnostic test accuracy. John Wiley & Sons, 2023.
  • [5] T. Badrick, ‘Biological variation: Understanding why it is so important?’, Pract. Lab. Med., vol. 23, p. e00199, Jan. 2021, doi: 10.1016/j.plabm.2020.e00199.
  • [6] A. Alanazi, ‘Using machine learning for healthcare challenges and opportunities’, Inform. Med. Unlocked, vol. 30, p. 100924, 2022.
  • [7] P. Sanchez, J. P. Voisey, T. Xia, H. I. Watson, A. Q. O’Neil, and S. A. Tsaftaris, ‘Causal machine learning for healthcare and precision medicine’, R. Soc. Open Sci., vol. 9, no. 8, p. 220638, 2022.
  • [8] M. Javaid, A. Haleem, R. P. Singh, R. Suman, and S. Rab, ‘Significance of machine learning in healthcare: Features, pillars and applications’, Int. J. Intell. Netw., vol. 3, pp. 58–73, 2022.
  • [9] S. Aminizadeh et al., ‘The applications of machine learning techniques in medical data processing based on distributed computing and the Internet of Things’, Comput. Methods Programs Biomed., p. 107745, 2023.
  • [10] A. Y. Gill, A. Saeed, S. Rasool, A. Husnain, and H. K. Hussain, ‘Revolutionizing Healthcare: How Machine Learning is Transforming Patient Diagnoses-a Comprehensive Review of AI’s Impact on Medical Diagnosis’, J. World Sci., vol. 2, no. 10, pp. 1638–1652, 2023.
  • [11] M. Shehab et al., ‘Machine learning in medical applications: A review of state-of-the-art methods’, Comput. Biol. Med., vol. 145, p. 105458, Jun. 2022, doi: 10.1016/j.compbiomed.2022.105458.
  • [12] K. Arumugam, M. Naved, P. P. Shinde, O. Leiva-Chauca, A. Huaman-Osorio, and T. Gonzales-Yanac, ‘Multiple disease prediction using Machine learning algorithms’, Mater. Today Proc., vol. 80, pp. 3682–3685, 2023.
  • [13] R. J. Means Jr et al., Wintrobe’s clinical hematology. Lippincott Williams & Wilkins, 2023.
  • [14] M. Auerbach, ‘Optimizing diagnosis and treatment of iron deficiency and iron deficiency anemia in women and girls of reproductive age: clinical opinion’, Int. J. Gynecol. Obstet., vol. 162, pp. 68–77, 2023.
  • [15] R. Shouval et al., ‘Validation of the acute leukemia‐EBMT score for prediction of mortality following allogeneic stem cell transplantation in a multi‐center GITMO cohort’, Am. J. Hematol., vol. 92, no. 5, pp. 429–434, May 2017, doi: 10.1002/ajh.24677.
  • [16] Y. Arai et al., ‘Using a machine learning algorithm to predict acute graft-versus-host disease following allogeneic transplantation’, Blood Adv., vol. 3, no. 22, pp. 3626–3634, Nov. 2019, doi: 10.1182/bloodadvances.2019000934.
  • [17] O. Gal, N. Auslander, Y. Fan, and D. Meerzaman, ‘Predicting Complete Remission of Acute Myeloid Leukemia: Machine Learning Applied to Gene Expression’, Cancer Inform., vol. 18, p. 117693511983554, Jan. 2019, doi: 10.1177/1176935119835544.
  • [18] G. Gunčar et al., ‘An application of machine learning to haematological diagnosis’, Sci. Rep., vol. 8, no. 1, p. 411, Jan. 2018, doi: 10.1038/s41598-017-18564-8.
  • [19] J. L. Malin, ‘Envisioning Watson As a Rapid-Learning System for Oncology’, J. Oncol. Pract., vol. 9, no. 3, pp. 155–157, May 2013, doi: 10.1200/JOP.2013.001021.
  • [20] M. Deulofeu et al., ‘Rapid discrimination of multiple myeloma patients by artificial neural networks coupled with mass spectrometry of peripheral blood plasma’, Sci. Rep., vol. 9, no. 1, p. 7975, May 2019, doi: 10.1038/s41598-019-44215-1.
  • [21] C. J. Haug and J. M. Drazen, ‘Artificial intelligence and machine learning in clinical medicine, 2023’, N. Engl. J. Med., vol. 388, no. 13, pp. 1201–1208, 2023.
  • [22] S. A. Alowais et al., ‘Revolutionizing healthcare: the role of artificial intelligence in clinical practice’, BMC Med. Educ., vol. 23, no. 1, p. 689, 2023.
  • [23] S. Palaniappan, R. V, B. David, and P. N. S, ‘Prediction of Epidemic Disease Dynamics on the Infection Risk Using Machine Learning Algorithms’, SN Comput. Sci., vol. 3, no. 1, p. 47, Jan. 2022, doi: 10.1007/s42979-021-00902-3.
  • [24] S. Roy, P. Biswas, and P. Ghosh, ‘Spatiotemporal tracing of pandemic spread from infection data’, Sci. Rep., vol. 11, no. 1, p. 17689, Sep. 2021, doi: 10.1038/s41598-021-97207-5.
  • [25] R. B. Ghannam and S. M. Techtmann, ‘Machine learning applications in microbial ecology, human microbiome studies, and environmental monitoring’, Comput. Struct. Biotechnol. J., vol. 19, pp. 1092–1107, 2021, doi: 10.1016/j.csbj.2021.01.028.
  • [26] S. Yadav, M. K. Singh, and S. Pal, ‘Artificial Intelligence Model for Parkinson Disease Detection Using Machine Learning Algorithms’, Biomed. Mater. Devices, Mar. 2023, doi: 10.1007/s44174-023-00068-x.
  • [27] J. A. Roth, M. Battegay, F. Juchler, J. E. Vogt, and A. F. Widmer, ‘Introduction to Machine Learning in Digital Healthcare Epidemiology’, Infect. Control Hosp. Epidemiol., vol. 39, no. 12, pp. 1457–1462, Dec. 2018, doi: 10.1017/ice.2018.265.
  • [28] R. Das, ‘A comparison of multiple classification methods for diagnosis of Parkinson disease’, Expert Syst. Appl., vol. 37, no. 2, pp. 1568–1572, Mar. 2010, doi: 10.1016/j.eswa.2009.06.040.
  • [29] A. Tsanas, M. A. Little, P. E. McSharry, and L. O. Ramig, ‘Nonlinear speech analysis algorithms mapped to a standard metric achieve clinically useful quantification of average Parkinson’s disease symptom severity’, J. R. Soc. Interface, vol. 8, no. 59, pp. 842–855, Jun. 2011, doi: 10.1098/rsif.2010.0456.
  • [30] M. A. Little and L. O. Ramig, ‘Suitability of dysphonia measurements for telemonitoring of Parkinson’s disease’, Nat. Preced., 2008.
  • [31] M. K. Gourisaria, S. Das, R. Sharma, S. S. Rautaray, and M. Pandey, ‘A deep learning model for malaria disease detection and analysis using deep convolutional neural networks’, Int. J. Emerg. Technol., vol. 11, no. 2, pp. 699–704, 2020.
  • [32] N. M. Deshpande, S. Gite, and R. Aluvalu, ‘A review of microscopic analysis of blood cells for disease detection with AI perspective’, PeerJ Comput. Sci., vol. 7, p. e460, 2021.
  • [33] D. N. Patil and U. P. Khot, ‘Image processing based abnormal blood cells detection’, Int. J. Tech. Res. Appl., vol. 31, pp. 37–43, 2015.
  • [34] R. Sigit, M. M. Bachtiar, and M. I. Fikri, ‘Identification of leukemia diseases based on microscopic human blood cells using image processing’, presented at the 2018 International Conference on Applied Engineering (ICAE), IEEE, 2018, pp. 1–5.
  • [35] P. K. Das, B. Nayak, and S. Meher, ‘A lightweight deep learning system for automatic detection of blood cancer’, Measurement, vol. 191, p. 110762, 2022.
  • [36] D. O. Oyewola, E. G. Dada, S. Misra, and R. Damaševičius, ‘A novel data augmentation convolutional neural network for detecting malaria parasite in blood smear images’, Appl. Artif. Intell., vol. 36, no. 1, p. 2033473, 2022.
  • [37] K. Gupta, N. Jiwani, and N. Afreen, ‘Blood pressure detection using CNN-LSTM model’, presented at the 2022 IEEE 11th International Conference on Communication Systems and Network Technologies (CSNT), IEEE, 2022, pp. 262–366.
  • [38] T. O. Kim et al., ‘Predicting Chronic Immune Thrombocytopenia in Pediatric Patients at Disease Presentation: Leveraging Clinical and Laboratory Characteristics Via Machine Learning Models’, Blood, vol. 138, p. 1023, 2021.
  • [39] Y. Cheng et al., ‘Using Machine Learning Algorithms to Predict Hospital Acquired Thrombocytopenia after Operation in the Intensive Care Unit: A Retrospective Cohort Study’, Diagnostics, vol. 11, no. 9, p. 1614, 2021.
  • [40] X.-H. Zhang et al., ‘P1652: Machine-Learning-Based Mortality Predıction of Ich In Adults With Itp: A Natıonwıde Representatıve Multicentre Study’, HemaSphere, vol. 6, no. Suppl, 2022.
  • [41] Y. Zhou et al., ‘Severe anemia, severe leukopenia, and severe thrombocytopenia of amphotericin B deoxycholate-based induction therapy in patients with HIV-associated talaromycosis: a subgroup analysis of a prospective multicenter cohort study’, BMC Infect. Dis., vol. 23, no. 1, p. 707, 2023.
  • [42] A. T. Johnsen, D. Tholstrup, M. A. Petersen, L. Pedersen, and M. Groenvold, ‘Health related quality of life in a nationally representative sample of haematological patients’, Eur. J. Haematol., vol. 83, no. 2, pp. 139–148, 2009.
  • [43] U. Jäger et al., ‘Diagnosis and treatment of autoimmune hemolytic anemia in adults: Recommendations from the First International Consensus Meeting’, Blood Rev., vol. 41, p. 100648, 2020.
  • [44] E. Franco, K. A. Karkoska, and P. T. McGann, ‘Inherited disorders of hemoglobin: A review of old and new diagnostic methods’, Blood Cells. Mol. Dis., p. 102758, 2023.
  • [45] E. Grudzińska and M. Modrzejewska, ‘Modern diagnostic techniques for the assessment of ocular blood flow in myopia: current state of knowledge’, J. Ophthalmol., vol. 2018, 2018.
  • [46] I. Voinsky, O. Y. Fridland, A. Aran, R. E. Frye, and D. Gurwitz, ‘Machine learning-based blood RNA signature for diagnosis of autism spectrum disorder’, Int. J. Mol. Sci., vol. 24, no. 3, p. 2082, 2023.
  • [47] S. Abd El-Ghany, M. Elmogy, and A. A. El-Aziz, ‘Computer-Aided Diagnosis System for Blood Diseases Using EfficientNet-B3 Based on a Dynamic Learning Algorithm’, Diagnostics, vol. 13, no. 3, p. 404, 2023.
  • [48] L. Pan et al., ‘Machine learning applications for prediction of relapse in childhood acute lymphoblastic leukemia’, Sci. Rep., vol. 7, no. 1, p. 7402, 2017.
  • [49] R. G. Hauser et al., ‘A machine learning model to successfully predict future diagnosis of chronic myelogenous leukemia with retrospective electronic health records data’, Am. J. Clin. Pathol., vol. 156, no. 6, pp. 1142–1148, 2021.
  • [50] P. Jagadev and D. H. G. Virani, "Detection of Leukemia and its Types using Image Processing and Machine Learning", 2017.
  • [51] H. Inbarani H., A. T. Azar, and J. G, ‘Leukemia Image Segmentation Using a Hybrid Histogram-Based Soft Covering Rough K-Means Clustering Algorithm’, Electronics, vol. 9, no. 1, p. 188, Jan. 2020, doi: 10.3390/electronics9010188.
  • [52] S. Kotsiantis, ‘Combining bagging, boosting, rotation forest and random subspace methods’, Artif. Intell. Rev., vol. 35, no. 3, pp. 223–240, Mar. 2011, doi: 10.1007/s10462-010-9192-8.
  • [53] J. Mielniczuk and P. Teisseyre, ‘Using random subspace method for prediction and variable importance assessment in linear regression’, Comput. Stat. Data Anal., vol. 71, pp. 725–742, Mar. 2014, doi: 10.1016/j.csda.2012.09.018.
  • [54] C. Bentéjac, A. Csörgő, and G. Martínez-Muñoz, ‘A comparative analysis of gradient boosting algorithms’, Artif. Intell. Rev., vol. 54, no. 3, pp. 1937–1967, Mar. 2021, doi: 10.1007/s10462-020-09896-5.
  • [55] F. Bulut, "Çok Katmanlı Algılayıcılar İle Doğru Meslek Tercihi", Anadolu Univ. J. Sci. Technol.- Appl. Sci. Eng., vol. 17, no. 1, Apr. 2016, doi: 10.18038/btda.45787.

Enhancing Early Detection of Blood Disorders through A Novel Hybrid Modeling Approach

Yıl 2023, Cilt: 12 Sayı: 4, 1261 - 1274, 28.12.2023

Pınar Karadayı Ataş

https://doi.org/10.17798/bitlisfen.1376817

Öz

Blood disorders are such conditions that impact the blood’s ability to function correctly. There is a range of different symptoms depending on the type. There are several different types of blood disorders such as Leukemia, chronic myelocytic leukemia, lymphoma, myelofibrosis, polycythemia, thrombocytopenia, anemia, and leukocytosis. Some resolve completely with therapy or do not cause symptoms and do not affect overall lifespan. Some are chronic and lifelong but do not affect how an individual lives. Other blood disorders, like sickle cell disease and blood cancers, can be even fatal. There needs to be a capture of hidden information in the medical data for detecting diseases in the early stages. This paper presents a novel hybrid modeling strategy that makes use of the synergy between two methods with histogram-based gradient boosting classifier tree and random subspace. It should be emphasized that the combination of these two models is being employed in this study for the first time. We present this novel model built for the assessment of blood diseases. The results show that the proposed model can predict the tumor of blood disease better than the other classifiers.

Anahtar Kelimeler

applied statistic statistical analysis and application structural and functional data machine learning.

Kaynakça

  • [1] H. Singh, A. N. D. Meyer, and E. J. Thomas, ‘The frequency of diagnostic errors in outpatient care: estimations from three large observational studies involving US adult populations’, BMJ Qual. Saf., vol. 23, no. 9, pp. 727–731, Sep. 2014, doi: 10.1136/bmjqs-2013-002627.
  • [2] T. M. Ghazal, A. U. Rehman, M. Saleem, M. Ahmad, S. Ahmad, and F. Mehmood, ‘Intelligent Model to Predict Early Liver Disease using Machine Learning Technique’, presented at the 2022 International Conference on Business Analytics for Technology and Security (ICBATS), IEEE, 2022, pp. 1–5.
  • [3] M. L. Graber, ‘The incidence of diagnostic error in medicine’, BMJ Qual. Saf., vol. 22, no. Suppl 2, pp. ii21–ii27, Oct. 2013, doi: 10.1136/bmjqs-2012-001615.
  • [4] J. J. Deeks, P. M. Bossuyt, M. M. Leeflang, and Y. Takwoingi, Cochrane handbook for systematic reviews of diagnostic test accuracy. John Wiley & Sons, 2023.
  • [5] T. Badrick, ‘Biological variation: Understanding why it is so important?’, Pract. Lab. Med., vol. 23, p. e00199, Jan. 2021, doi: 10.1016/j.plabm.2020.e00199.
  • [6] A. Alanazi, ‘Using machine learning for healthcare challenges and opportunities’, Inform. Med. Unlocked, vol. 30, p. 100924, 2022.
  • [7] P. Sanchez, J. P. Voisey, T. Xia, H. I. Watson, A. Q. O’Neil, and S. A. Tsaftaris, ‘Causal machine learning for healthcare and precision medicine’, R. Soc. Open Sci., vol. 9, no. 8, p. 220638, 2022.
  • [8] M. Javaid, A. Haleem, R. P. Singh, R. Suman, and S. Rab, ‘Significance of machine learning in healthcare: Features, pillars and applications’, Int. J. Intell. Netw., vol. 3, pp. 58–73, 2022.
  • [9] S. Aminizadeh et al., ‘The applications of machine learning techniques in medical data processing based on distributed computing and the Internet of Things’, Comput. Methods Programs Biomed., p. 107745, 2023.
  • [10] A. Y. Gill, A. Saeed, S. Rasool, A. Husnain, and H. K. Hussain, ‘Revolutionizing Healthcare: How Machine Learning is Transforming Patient Diagnoses-a Comprehensive Review of AI’s Impact on Medical Diagnosis’, J. World Sci., vol. 2, no. 10, pp. 1638–1652, 2023.
  • [11] M. Shehab et al., ‘Machine learning in medical applications: A review of state-of-the-art methods’, Comput. Biol. Med., vol. 145, p. 105458, Jun. 2022, doi: 10.1016/j.compbiomed.2022.105458.
  • [12] K. Arumugam, M. Naved, P. P. Shinde, O. Leiva-Chauca, A. Huaman-Osorio, and T. Gonzales-Yanac, ‘Multiple disease prediction using Machine learning algorithms’, Mater. Today Proc., vol. 80, pp. 3682–3685, 2023.
  • [13] R. J. Means Jr et al., Wintrobe’s clinical hematology. Lippincott Williams & Wilkins, 2023.
  • [14] M. Auerbach, ‘Optimizing diagnosis and treatment of iron deficiency and iron deficiency anemia in women and girls of reproductive age: clinical opinion’, Int. J. Gynecol. Obstet., vol. 162, pp. 68–77, 2023.
  • [15] R. Shouval et al., ‘Validation of the acute leukemia‐EBMT score for prediction of mortality following allogeneic stem cell transplantation in a multi‐center GITMO cohort’, Am. J. Hematol., vol. 92, no. 5, pp. 429–434, May 2017, doi: 10.1002/ajh.24677.
  • [16] Y. Arai et al., ‘Using a machine learning algorithm to predict acute graft-versus-host disease following allogeneic transplantation’, Blood Adv., vol. 3, no. 22, pp. 3626–3634, Nov. 2019, doi: 10.1182/bloodadvances.2019000934.
  • [17] O. Gal, N. Auslander, Y. Fan, and D. Meerzaman, ‘Predicting Complete Remission of Acute Myeloid Leukemia: Machine Learning Applied to Gene Expression’, Cancer Inform., vol. 18, p. 117693511983554, Jan. 2019, doi: 10.1177/1176935119835544.
  • [18] G. Gunčar et al., ‘An application of machine learning to haematological diagnosis’, Sci. Rep., vol. 8, no. 1, p. 411, Jan. 2018, doi: 10.1038/s41598-017-18564-8.
  • [19] J. L. Malin, ‘Envisioning Watson As a Rapid-Learning System for Oncology’, J. Oncol. Pract., vol. 9, no. 3, pp. 155–157, May 2013, doi: 10.1200/JOP.2013.001021.
  • [20] M. Deulofeu et al., ‘Rapid discrimination of multiple myeloma patients by artificial neural networks coupled with mass spectrometry of peripheral blood plasma’, Sci. Rep., vol. 9, no. 1, p. 7975, May 2019, doi: 10.1038/s41598-019-44215-1.
  • [21] C. J. Haug and J. M. Drazen, ‘Artificial intelligence and machine learning in clinical medicine, 2023’, N. Engl. J. Med., vol. 388, no. 13, pp. 1201–1208, 2023.
  • [22] S. A. Alowais et al., ‘Revolutionizing healthcare: the role of artificial intelligence in clinical practice’, BMC Med. Educ., vol. 23, no. 1, p. 689, 2023.
  • [23] S. Palaniappan, R. V, B. David, and P. N. S, ‘Prediction of Epidemic Disease Dynamics on the Infection Risk Using Machine Learning Algorithms’, SN Comput. Sci., vol. 3, no. 1, p. 47, Jan. 2022, doi: 10.1007/s42979-021-00902-3.
  • [24] S. Roy, P. Biswas, and P. Ghosh, ‘Spatiotemporal tracing of pandemic spread from infection data’, Sci. Rep., vol. 11, no. 1, p. 17689, Sep. 2021, doi: 10.1038/s41598-021-97207-5.
  • [25] R. B. Ghannam and S. M. Techtmann, ‘Machine learning applications in microbial ecology, human microbiome studies, and environmental monitoring’, Comput. Struct. Biotechnol. J., vol. 19, pp. 1092–1107, 2021, doi: 10.1016/j.csbj.2021.01.028.
  • [26] S. Yadav, M. K. Singh, and S. Pal, ‘Artificial Intelligence Model for Parkinson Disease Detection Using Machine Learning Algorithms’, Biomed. Mater. Devices, Mar. 2023, doi: 10.1007/s44174-023-00068-x.
  • [27] J. A. Roth, M. Battegay, F. Juchler, J. E. Vogt, and A. F. Widmer, ‘Introduction to Machine Learning in Digital Healthcare Epidemiology’, Infect. Control Hosp. Epidemiol., vol. 39, no. 12, pp. 1457–1462, Dec. 2018, doi: 10.1017/ice.2018.265.
  • [28] R. Das, ‘A comparison of multiple classification methods for diagnosis of Parkinson disease’, Expert Syst. Appl., vol. 37, no. 2, pp. 1568–1572, Mar. 2010, doi: 10.1016/j.eswa.2009.06.040.
  • [29] A. Tsanas, M. A. Little, P. E. McSharry, and L. O. Ramig, ‘Nonlinear speech analysis algorithms mapped to a standard metric achieve clinically useful quantification of average Parkinson’s disease symptom severity’, J. R. Soc. Interface, vol. 8, no. 59, pp. 842–855, Jun. 2011, doi: 10.1098/rsif.2010.0456.
  • [30] M. A. Little and L. O. Ramig, ‘Suitability of dysphonia measurements for telemonitoring of Parkinson’s disease’, Nat. Preced., 2008.
  • [31] M. K. Gourisaria, S. Das, R. Sharma, S. S. Rautaray, and M. Pandey, ‘A deep learning model for malaria disease detection and analysis using deep convolutional neural networks’, Int. J. Emerg. Technol., vol. 11, no. 2, pp. 699–704, 2020.
  • [32] N. M. Deshpande, S. Gite, and R. Aluvalu, ‘A review of microscopic analysis of blood cells for disease detection with AI perspective’, PeerJ Comput. Sci., vol. 7, p. e460, 2021.
  • [33] D. N. Patil and U. P. Khot, ‘Image processing based abnormal blood cells detection’, Int. J. Tech. Res. Appl., vol. 31, pp. 37–43, 2015.
  • [34] R. Sigit, M. M. Bachtiar, and M. I. Fikri, ‘Identification of leukemia diseases based on microscopic human blood cells using image processing’, presented at the 2018 International Conference on Applied Engineering (ICAE), IEEE, 2018, pp. 1–5.
  • [35] P. K. Das, B. Nayak, and S. Meher, ‘A lightweight deep learning system for automatic detection of blood cancer’, Measurement, vol. 191, p. 110762, 2022.
  • [36] D. O. Oyewola, E. G. Dada, S. Misra, and R. Damaševičius, ‘A novel data augmentation convolutional neural network for detecting malaria parasite in blood smear images’, Appl. Artif. Intell., vol. 36, no. 1, p. 2033473, 2022.
  • [37] K. Gupta, N. Jiwani, and N. Afreen, ‘Blood pressure detection using CNN-LSTM model’, presented at the 2022 IEEE 11th International Conference on Communication Systems and Network Technologies (CSNT), IEEE, 2022, pp. 262–366.
  • [38] T. O. Kim et al., ‘Predicting Chronic Immune Thrombocytopenia in Pediatric Patients at Disease Presentation: Leveraging Clinical and Laboratory Characteristics Via Machine Learning Models’, Blood, vol. 138, p. 1023, 2021.
  • [39] Y. Cheng et al., ‘Using Machine Learning Algorithms to Predict Hospital Acquired Thrombocytopenia after Operation in the Intensive Care Unit: A Retrospective Cohort Study’, Diagnostics, vol. 11, no. 9, p. 1614, 2021.
  • [40] X.-H. Zhang et al., ‘P1652: Machine-Learning-Based Mortality Predıction of Ich In Adults With Itp: A Natıonwıde Representatıve Multicentre Study’, HemaSphere, vol. 6, no. Suppl, 2022.
  • [41] Y. Zhou et al., ‘Severe anemia, severe leukopenia, and severe thrombocytopenia of amphotericin B deoxycholate-based induction therapy in patients with HIV-associated talaromycosis: a subgroup analysis of a prospective multicenter cohort study’, BMC Infect. Dis., vol. 23, no. 1, p. 707, 2023.
  • [42] A. T. Johnsen, D. Tholstrup, M. A. Petersen, L. Pedersen, and M. Groenvold, ‘Health related quality of life in a nationally representative sample of haematological patients’, Eur. J. Haematol., vol. 83, no. 2, pp. 139–148, 2009.
  • [43] U. Jäger et al., ‘Diagnosis and treatment of autoimmune hemolytic anemia in adults: Recommendations from the First International Consensus Meeting’, Blood Rev., vol. 41, p. 100648, 2020.
  • [44] E. Franco, K. A. Karkoska, and P. T. McGann, ‘Inherited disorders of hemoglobin: A review of old and new diagnostic methods’, Blood Cells. Mol. Dis., p. 102758, 2023.
  • [45] E. Grudzińska and M. Modrzejewska, ‘Modern diagnostic techniques for the assessment of ocular blood flow in myopia: current state of knowledge’, J. Ophthalmol., vol. 2018, 2018.
  • [46] I. Voinsky, O. Y. Fridland, A. Aran, R. E. Frye, and D. Gurwitz, ‘Machine learning-based blood RNA signature for diagnosis of autism spectrum disorder’, Int. J. Mol. Sci., vol. 24, no. 3, p. 2082, 2023.
  • [47] S. Abd El-Ghany, M. Elmogy, and A. A. El-Aziz, ‘Computer-Aided Diagnosis System for Blood Diseases Using EfficientNet-B3 Based on a Dynamic Learning Algorithm’, Diagnostics, vol. 13, no. 3, p. 404, 2023.
  • [48] L. Pan et al., ‘Machine learning applications for prediction of relapse in childhood acute lymphoblastic leukemia’, Sci. Rep., vol. 7, no. 1, p. 7402, 2017.
  • [49] R. G. Hauser et al., ‘A machine learning model to successfully predict future diagnosis of chronic myelogenous leukemia with retrospective electronic health records data’, Am. J. Clin. Pathol., vol. 156, no. 6, pp. 1142–1148, 2021.
  • [50] P. Jagadev and D. H. G. Virani, "Detection of Leukemia and its Types using Image Processing and Machine Learning", 2017.
  • [51] H. Inbarani H., A. T. Azar, and J. G, ‘Leukemia Image Segmentation Using a Hybrid Histogram-Based Soft Covering Rough K-Means Clustering Algorithm’, Electronics, vol. 9, no. 1, p. 188, Jan. 2020, doi: 10.3390/electronics9010188.
  • [52] S. Kotsiantis, ‘Combining bagging, boosting, rotation forest and random subspace methods’, Artif. Intell. Rev., vol. 35, no. 3, pp. 223–240, Mar. 2011, doi: 10.1007/s10462-010-9192-8.
  • [53] J. Mielniczuk and P. Teisseyre, ‘Using random subspace method for prediction and variable importance assessment in linear regression’, Comput. Stat. Data Anal., vol. 71, pp. 725–742, Mar. 2014, doi: 10.1016/j.csda.2012.09.018.
  • [54] C. Bentéjac, A. Csörgő, and G. Martínez-Muñoz, ‘A comparative analysis of gradient boosting algorithms’, Artif. Intell. Rev., vol. 54, no. 3, pp. 1937–1967, Mar. 2021, doi: 10.1007/s10462-020-09896-5.
  • [55] F. Bulut, "Çok Katmanlı Algılayıcılar İle Doğru Meslek Tercihi", Anadolu Univ. J. Sci. Technol.- Appl. Sci. Eng., vol. 17, no. 1, Apr. 2016, doi: 10.18038/btda.45787.
Toplam 55 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Yapay Zeka (Diğer), Biyoinformatik Yöntem Geliştirme, Biyoinformatik ve Hesaplamalı Biyoloji (Diğer), Genetik (Diğer), Biyomedikal Bilimler ve Teknolojiler, Biyomedikal Tanı
Bölüm Araştırma Makalesi
Yazarlar

Pınar Karadayı Ataş 0000-0002-9429-8463

Erken Görünüm Tarihi 25 Aralık 2023
Yayımlanma Tarihi 28 Aralık 2023
Gönderilme Tarihi 16 Ekim 2023
Kabul Tarihi 4 Aralık 2023
Yayımlandığı Sayı Yıl 2023 Cilt: 12 Sayı: 4

Kaynak Göster

IEEE P. Karadayı Ataş, “Enhancing Early Detection of Blood Disorders through A Novel Hybrid Modeling Approach”, Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, c. 12, sy. 4, ss. 1261–1274, 2023, doi: 10.17798/bitlisfen.1376817.
 Kapak Resmi İndir



Bitlis Eren Üniversitesi
Fen Bilimleri Dergisi Editörlüğü

Bitlis Eren Üniversitesi Lisansüstü Eğitim Enstitüsü        
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E-posta: fbe@beu.edu.tr