Cascade-Based Input-Doubling Classifier for Predicting Survival in Allogeneic Bone Marrow Transplants: Small Data Case
In the field of transplantology, where medical decisions are heavily dependent on complex data analysis, the challenge of small data has become increasingly prominent. Transplantology, which focuses on the transplantation of organs and tissues, requires exceptional accuracy and precision in predicti...
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| Language: | English |
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MDPI AG
2025-03-01
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| Series: | Computation |
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| Online Access: | https://www.mdpi.com/2079-3197/13/4/80 |
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| author | Ivan Izonin Roman Tkachenko Nazarii Hovdysh Oleh Berezsky Kyrylo Yemets Ivan Tsmots |
| author_facet | Ivan Izonin Roman Tkachenko Nazarii Hovdysh Oleh Berezsky Kyrylo Yemets Ivan Tsmots |
| author_sort | Ivan Izonin |
| collection | DOAJ |
| description | In the field of transplantology, where medical decisions are heavily dependent on complex data analysis, the challenge of small data has become increasingly prominent. Transplantology, which focuses on the transplantation of organs and tissues, requires exceptional accuracy and precision in predicting outcomes, assessing risks, and tailoring treatment plans. However, the inherent limitations of small datasets present significant obstacles. This paper introduces an advanced input-doubling classifier designed to improve survival predictions for allogeneic bone marrow transplants. The approach utilizes two artificial intelligence tools: the first Probabilistic Neural Network generates output signals that expand the independent attributes of an augmented dataset, while the second machine learning algorithm performs the final classification. This method, based on the cascading principle, facilitates the development of novel algorithms for preparing and applying the enhanced input-doubling technique to classification tasks. The proposed method was tested on a small dataset within transplantology, focusing on binary classification. Optimal parameters for the method were identified using the Dual Annealing algorithm. Comparative analysis of the improved method against several existing approaches revealed a substantial improvement in accuracy across various performance metrics, underscoring its practical benefits |
| format | Article |
| id | doaj-art-04fe350e9afb4cb58f0ecc62ebbd83bc |
| institution | DOAJ |
| issn | 2079-3197 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computation |
| spelling | doaj-art-04fe350e9afb4cb58f0ecc62ebbd83bc2025-08-20T03:13:44ZengMDPI AGComputation2079-31972025-03-011348010.3390/computation13040080Cascade-Based Input-Doubling Classifier for Predicting Survival in Allogeneic Bone Marrow Transplants: Small Data CaseIvan Izonin0Roman Tkachenko1Nazarii Hovdysh2Oleh Berezsky3Kyrylo Yemets4Ivan Tsmots5Department of Artificial Intelligence, Lviv Polytechnic National University, 79013 Lviv, UkraineDepartment of Publishing Information Technologies, Lviv Polytechnic National University, 79013 Lviv, UkraineDepartment of Artificial Intelligence, Lviv Polytechnic National University, 79013 Lviv, UkraineDepartment of Computer Engineering, West Ukrainian National University, Lvivska, 11, 46003 Ternopil, UkraineDepartment of Artificial Intelligence, Lviv Polytechnic National University, 79013 Lviv, UkraineDepartment of Automated Control Systems, Lviv Polytechnic National University, 79013 Lviv, UkraineIn the field of transplantology, where medical decisions are heavily dependent on complex data analysis, the challenge of small data has become increasingly prominent. Transplantology, which focuses on the transplantation of organs and tissues, requires exceptional accuracy and precision in predicting outcomes, assessing risks, and tailoring treatment plans. However, the inherent limitations of small datasets present significant obstacles. This paper introduces an advanced input-doubling classifier designed to improve survival predictions for allogeneic bone marrow transplants. The approach utilizes two artificial intelligence tools: the first Probabilistic Neural Network generates output signals that expand the independent attributes of an augmented dataset, while the second machine learning algorithm performs the final classification. This method, based on the cascading principle, facilitates the development of novel algorithms for preparing and applying the enhanced input-doubling technique to classification tasks. The proposed method was tested on a small dataset within transplantology, focusing on binary classification. Optimal parameters for the method were identified using the Dual Annealing algorithm. Comparative analysis of the improved method against several existing approaches revealed a substantial improvement in accuracy across various performance metrics, underscoring its practical benefitshttps://www.mdpi.com/2079-3197/13/4/80small data approachinput-doubling methodclassification taskimbalanced datasettransplantation medicinemachine learning |
| spellingShingle | Ivan Izonin Roman Tkachenko Nazarii Hovdysh Oleh Berezsky Kyrylo Yemets Ivan Tsmots Cascade-Based Input-Doubling Classifier for Predicting Survival in Allogeneic Bone Marrow Transplants: Small Data Case Computation small data approach input-doubling method classification task imbalanced dataset transplantation medicine machine learning |
| title | Cascade-Based Input-Doubling Classifier for Predicting Survival in Allogeneic Bone Marrow Transplants: Small Data Case |
| title_full | Cascade-Based Input-Doubling Classifier for Predicting Survival in Allogeneic Bone Marrow Transplants: Small Data Case |
| title_fullStr | Cascade-Based Input-Doubling Classifier for Predicting Survival in Allogeneic Bone Marrow Transplants: Small Data Case |
| title_full_unstemmed | Cascade-Based Input-Doubling Classifier for Predicting Survival in Allogeneic Bone Marrow Transplants: Small Data Case |
| title_short | Cascade-Based Input-Doubling Classifier for Predicting Survival in Allogeneic Bone Marrow Transplants: Small Data Case |
| title_sort | cascade based input doubling classifier for predicting survival in allogeneic bone marrow transplants small data case |
| topic | small data approach input-doubling method classification task imbalanced dataset transplantation medicine machine learning |
| url | https://www.mdpi.com/2079-3197/13/4/80 |
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