The Comparison of Classical Statistical and Machine Learning Methods in Prediction of Thrombosis in Patients with Acute Myeloid Leukemia
Thrombosis is one of the most frequent complications of cancer, with a potential impact on morbidity and mortality, particularly those with acute myeloid leukemia (AML). Therefore, effective thrombosis prevention is a crucial aspect of cancer management. However, preventive measures against thrombos...
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2025-01-01
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| author | Ilija Doknić Mirjana Mitrović Zoran Bukumirić Marijana Virijević Nikola Pantić Nikica Sabljić Darko Antić Živko Bojović |
| author_facet | Ilija Doknić Mirjana Mitrović Zoran Bukumirić Marijana Virijević Nikola Pantić Nikica Sabljić Darko Antić Živko Bojović |
| author_sort | Ilija Doknić |
| collection | DOAJ |
| description | Thrombosis is one of the most frequent complications of cancer, with a potential impact on morbidity and mortality, particularly those with acute myeloid leukemia (AML). Therefore, effective thrombosis prevention is a crucial aspect of cancer management. However, preventive measures against thrombosis may carry inherent risks and complications. Consequently, the application of thrombosis prevention should be limited to patients with a reasonable risk of developing thrombosis. This thesis explores the potential of data science (DS) methods for predicting venous thrombosis in patients with acute myeloid leukemia. In order to ascertain which patients are at risk, statistical and machine-learning (ML) algorithms were employed to predict which patients with leukemia will develop thrombosis. Multilayer Perceptron (MLP) was found to be the best fit among the models evaluated, achieving the C statistic of 0.749. We examined which attributes are significant and what role they play in prediction and found six significant parameters: sex of the patient, prior history of thrombotic event, type of therapy, international normalized ratio (INR), Eastern Cooperative Oncology Group (ECOG) performance status, and Hematopoietic Cell Transplantation-specific Comorbidity. These findings suggest that subtle DS techniques can improve the prediction of Thrombosis in AML patients, thereby aiding in individual treatment planning. |
| format | Article |
| id | doaj-art-8ec35172f14a48d395872aa3bc287753 |
| institution | Kabale University |
| issn | 2306-5354 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Bioengineering |
| spelling | doaj-art-8ec35172f14a48d395872aa3bc2877532025-01-24T13:23:08ZengMDPI AGBioengineering2306-53542025-01-011216310.3390/bioengineering12010063The Comparison of Classical Statistical and Machine Learning Methods in Prediction of Thrombosis in Patients with Acute Myeloid LeukemiaIlija Doknić0Mirjana Mitrović1Zoran Bukumirić2Marijana Virijević3Nikola Pantić4Nikica Sabljić5Darko Antić6Živko Bojović7Faculty of Sciences, University of Novi Sad, 21000 Novi Sad, SerbiaClinic of Hematology, University Clinical Center of Serbia, 11000 Belgrade, SerbiaFaculty of Medicine, University of Belgrade, 11000 Belgrade, SerbiaClinic of Hematology, University Clinical Center of Serbia, 11000 Belgrade, SerbiaClinic of Hematology, University Clinical Center of Serbia, 11000 Belgrade, SerbiaClinic of Hematology, University Clinical Center of Serbia, 11000 Belgrade, SerbiaClinic of Hematology, University Clinical Center of Serbia, 11000 Belgrade, SerbiaFaculty of Informatics and Computing, Singidunum University, 11000 Belgrade, SerbiaThrombosis is one of the most frequent complications of cancer, with a potential impact on morbidity and mortality, particularly those with acute myeloid leukemia (AML). Therefore, effective thrombosis prevention is a crucial aspect of cancer management. However, preventive measures against thrombosis may carry inherent risks and complications. Consequently, the application of thrombosis prevention should be limited to patients with a reasonable risk of developing thrombosis. This thesis explores the potential of data science (DS) methods for predicting venous thrombosis in patients with acute myeloid leukemia. In order to ascertain which patients are at risk, statistical and machine-learning (ML) algorithms were employed to predict which patients with leukemia will develop thrombosis. Multilayer Perceptron (MLP) was found to be the best fit among the models evaluated, achieving the C statistic of 0.749. We examined which attributes are significant and what role they play in prediction and found six significant parameters: sex of the patient, prior history of thrombotic event, type of therapy, international normalized ratio (INR), Eastern Cooperative Oncology Group (ECOG) performance status, and Hematopoietic Cell Transplantation-specific Comorbidity. These findings suggest that subtle DS techniques can improve the prediction of Thrombosis in AML patients, thereby aiding in individual treatment planning.https://www.mdpi.com/2306-5354/12/1/63thrombosisacute myeloid leukemiamachine learningneural networksclassical statistical methods |
| spellingShingle | Ilija Doknić Mirjana Mitrović Zoran Bukumirić Marijana Virijević Nikola Pantić Nikica Sabljić Darko Antić Živko Bojović The Comparison of Classical Statistical and Machine Learning Methods in Prediction of Thrombosis in Patients with Acute Myeloid Leukemia Bioengineering thrombosis acute myeloid leukemia machine learning neural networks classical statistical methods |
| title | The Comparison of Classical Statistical and Machine Learning Methods in Prediction of Thrombosis in Patients with Acute Myeloid Leukemia |
| title_full | The Comparison of Classical Statistical and Machine Learning Methods in Prediction of Thrombosis in Patients with Acute Myeloid Leukemia |
| title_fullStr | The Comparison of Classical Statistical and Machine Learning Methods in Prediction of Thrombosis in Patients with Acute Myeloid Leukemia |
| title_full_unstemmed | The Comparison of Classical Statistical and Machine Learning Methods in Prediction of Thrombosis in Patients with Acute Myeloid Leukemia |
| title_short | The Comparison of Classical Statistical and Machine Learning Methods in Prediction of Thrombosis in Patients with Acute Myeloid Leukemia |
| title_sort | comparison of classical statistical and machine learning methods in prediction of thrombosis in patients with acute myeloid leukemia |
| topic | thrombosis acute myeloid leukemia machine learning neural networks classical statistical methods |
| url | https://www.mdpi.com/2306-5354/12/1/63 |
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