Optimized Machine Learning Pipeline for Lung Cancer Classification: Feature Reduction and Hyperparameter Tuning
The proposed optimized ML pipeline enhances lung cancer classification accuracy using routine clinical data. The results highlight that simpler, well-optimized models can outperform complex approaches on structured datasets. This framework shows strong potential for early lung cancer risk screening and clinical decision support, although further validation on larger datasets is recommended.
Authors
- Gufran Ahmad Ansari (ORCID: https://orcid.org/0000-0002-7387-5962)
- Lamees Alhazzaa
- Salliah Shafi (ORCID: https://orcid.org/0009-0002-9768-7760)
Institutions
- Imam Mohammad ibn Saud Islamic University (SA)
- GNA University (IN)
Publication Details
- Journal
- Diagnostics
- Published
- 2026-04-17
- DOI
- https://doi.org/10.3390/diagnostics16081198
- Primary Topic
- Lung Cancer Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Al-Imam Muhammad Ibn Saud Islamic University