Comparison of Proposed Model, RF, SVM, DNN, and RNN models using Recall for Class 0 on imbalanced datasets.

Comparison of Proposed Model, RF, SVM, DNN, and RNN models using Recall for Class 0 on imbalanced datasets.

<p>Comparison of Proposed Model, RF, SVM, DNN, and RNN models using Recall for Class 0 on imbalanced datasets.</p>

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Bibliographic Details
Main Author:	Saim Chishti (22272188) (author)
Other Authors:	Faryal Nosheen (22272191) (author), Joddat Fatima (22272194) (author), Nadia Sultan (11268617) (author), Madiha Khalid (10196725) (author)
Published:	2025
Subjects:	Genetics Biotechnology Cancer Science Policy Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified support vector machine inherited blood disorder identifying hidden patterns deep neural networks corresponding globin chains machine learning algorithms accurately detect carriers xlink "> thalassemia thalassemia &# 8212 successfully identified beta normal test reports achieve high accuracy complex medical data models perform classification medical data thalassemia carriers various models unseen data normalized data based algorithms thalassemia carrier substantial changes southeast asia related complications prevalent birth past couple interpretable detection increasingly focused genetic testing effectively analyze decision tree class normalization accurate diagnosis accuracy ). absent production abnormal ),
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