The importance of detecting and preventing ovarian cancer is of utmost significance for women’s overall health and wellness. Referred to as the “silent killer,” ovarian cancer exhibits inconspicuous symptoms during its initial phases, posing a challenge for timely identification. Identification of ovarian cancer during its advanced stages significantly diminishes the likelihood of effective treatment and survival. Regular screenings, such as pelvic exams, ultrasound, and blood tests for specific biomarkers, are essential tools for detecting the disease in its early, more treatable stages. This research makes use of the Soochow University ovarian cancer dataset, containing 50 features for the accurate detection of ovarian cancer. The proposed predictive model makes use of a stacked ensemble model, merging the strengths of bagging and boosting classifiers, and aims to enhance predictive accuracy and reliability. This combination harnesses the benefits of variance reduction and improved generalization, contributing to superior ovarian cancer prediction outcomes. The proposed model gives 96.87% accuracy, which is currently the highest model result obtained on this dataset so far using all features. Moreover, the outcomes are elucidated utilizing the explainable artificial intelligence method referred to as SHAPly. The excellence of the suggested model is demonstrated through a comparison of its performance with that of other cutting-edge models.
卵巢癌的检测与预防对女性整体健康至关重要。作为"沉默的杀手"，卵巢癌早期症状隐匿，给及时诊断带来挑战。晚期卵巢癌的发现会显著降低有效治疗和生存的可能性。定期筛查，如盆腔检查、超声及特定生物标志物血液检测，是早期发现这种可治疗疾病的重要手段。本研究采用苏州大学卵巢癌数据集，包含50个特征以实现卵巢癌的精准检测。提出的预测模型采用堆叠集成方法，融合了装袋与提升分类器的优势，旨在提升预测准确性与可靠性。该组合通过降低方差和增强泛化能力，实现了优异的卵巢癌预测效果。所提模型准确率达96.87%，是目前该数据集所有特征应用中的最佳结果。此外，研究采用可解释人工智能方法SHAP对结果进行阐释，并通过与前沿模型的性能对比，验证了所提模型的优越性。
Improved Prediction of Ovarian Cancer Using Ensemble Classifier and Shaply Explainable AI
肿瘤(癌症)患者之家