An efficient interpretable stacking ensemble model for lung cancer prognosis

Lung cancer significantly contributes to global cancer mortality, posing challenges in clinical management. Early detection and accurate prognosis are crucial for improving patient outcomes. This study develops an interpretable stacking ensemble model (SEM) for lung cancer prognosis prediction and i...

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Veröffentlicht in:	Computational biology and chemistry 2024-12, Vol.113, p.108248, Article 108248
Hauptverfasser:	Arif, Umair, Zhang, Chunxia, Hussain, Sajid, Abbasi, Abdul Rauf
Format:	Artikel
Sprache:	eng
Schlagworte:	Ensemble learning Humans Local interpretable model-agnostic explanations Lung cancer prediction Lung Neoplasms - diagnosis Lung Neoplasms - pathology Machine learning Prognosis Risk Factors Shapley additive explanations
Online-Zugang:	Volltext
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