Explainable Ensemble Learning Approach for Ovarian Cancer Diagnosis Using Clinical Data

Daniyal Asif; Nabil Kerdid; Muhammad Shoaib Arif; Mairaj Bibi

doi:10.32604/cmes.2026.077334

January 1, 2026Computer Modeling in Engineering & SciencesOpen Access

Explainable Ensemble Learning Approach for Ovarian Cancer Diagnosis Using Clinical Data

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Key Points

The research aims to improve early diagnosis of ovarian cancer using explainable machine learning techniques.
Utilized clinical data for model training
Employed ensemble learning algorithms
Analyzed the performance of models on diagnostic accuracy
Improved accuracy in ovarian cancer diagnosis compared to traditional methods
Developed models provide insights into important clinical features
Demonstrated potential for early detection of ovarian cancer

Implication

This analysis demonstrates enhanced ovarian cancer diagnosis using machine learning, suggesting improved outcomes for early detection.

Abstract

Ovarian cancer (OC) is one of the leading causes of death related to gynecological cancer, with the main difficulty of its early diagnosis and a heterogeneous nature of tumor biomarkers. Machine learning (ML) has the potentia... | Find, read and cite all the research you need on Tech Science Press

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Cite This Study

Asif et al. (2026) studied this question.

synapsesocial.com/papers/69cd7b065652765b073a8a44 https://doi.org/10.32604/cmes.2026.077334

View Full Paper

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Explainable Ensemble Learning Approach for Ovarian Cancer Diagnosis Using Clinical Data

Key Points

Implication

Abstract

Discussion

Cite This Study

Explainable Ensemble Learning Approach for Ovarian Cancer Diagnosis Using Clinical Data

Key Points

Implication

Abstract

Discussion

Cite This Study

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