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.

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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

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article

Optimized Machine Learning Pipeline for Lung Cancer Classification: Feature Reduction and Hyperparameter Tuning

Gufran Ahmad Ansari, Lamees Alhazzaa, Salliah Shafi
Diagnostics
Lung Cancer Diagnosis and Treatment
article

Optimized Machine Learning Pipeline for Lung Cancer Classification: Feature Reduction and Hyperparameter Tuning

Gufran Ahmad Ansari, Lamees Alhazzaa, Salliah Shafi
article en

Abstract

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.

DiagnosticsVol. 16(8)
Imam Mohammad ibn Saud Islamic University (SA), GNA University (IN)
Al-Imam Muhammad Ibn Saud Islamic University
Good health and well-being
Openalex Percentile: Top 21%
Lung Cancer Diagnosis and Treatment
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