Non-Invasive Cancer Detection Using Blood Test and Predictive Modeling Approach
After investigating a number of machine learning methods, an efficient screening platform for non-invasive cancer detection is provided by the integration of haematological indicators with proper analytical data. Exploring deep learning methods in the future work, could provide insights into more complex patterns within the dataset, potentially improving the accuracy and robustness of the predictions.
Authors
- Ibrahim S. Alkhazi (ORCID: https://orcid.org/0000-0001-8447-3486)
- Ahmad B. Hassanat (ORCID: https://orcid.org/0000-0002-9991-304X)
- Enas Al-khlifeh
- Mansoor Alghamdi (ORCID: https://orcid.org/0000-0002-2891-6374)
- Majed Abdullah Alrowaily (ORCID: https://orcid.org/0000-0002-8537-8795)
- Ahmad S. Tarawneh
- Ahmad Al Omari
- Fatimah Tarawneh
Institutions
- University of Jordan (JO)
- Jouf University (SA)
- Mutah University (JO)
- King Hussein Medical Center (JO)
- Al-Balqa Applied University (JO)
- University of Tabuk (SA)
Publication Details
- Journal
- Advances and Applications in Bioinformatics and Chemistry
- Published
- 2025-01-01
- DOI
- https://doi.org/10.2147/aabc.s488604
- Citations
- 1
- Primary Topic
- Imbalanced Data Classification Techniques
- Type
- article
- Field-Weighted Citation Impact
- 2.48