Prostate Cancer Classification Using Multimodal Feature Fusion and Explainable AI

• URL: http://arxiv.org/abs/2507.20714v1
• Date: Mon, 28 Jul 2025 11:07:17 GMT
• Title: Prostate Cancer Classification Using Multimodal Feature Fusion and Explainable AI
• Authors: Asma Sadia Khan, Fariba Tasnia Khan, Tanjim Mahmud, Salman Karim Khan, Rishita Chakma, Nahed Sharmen, Mohammad Shahadat Hossain, Karl Andersson,
• Abstract summary: We propose an explainable AI system combining BERT (for textual clinical notes) and Random Forest (for numerical lab data)<n>Our work demonstrates that a simple yet interpretable BERT+RF pipeline delivers clinically significant improvements.
• Score: 2.656041527404895
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Prostate cancer, the second most prevalent male malignancy, requires advanced diagnostic tools. We propose an explainable AI system combining BERT (for textual clinical notes) and Random Forest (for numerical lab data) through a novel multimodal fusion strategy, achieving superior classification performance on PLCO-NIH dataset (98% accuracy, 99% AUC). While multimodal fusion is established, our work demonstrates that a simple yet interpretable BERT+RF pipeline delivers clinically significant improvements - particularly for intermediate cancer stages (Class 2/3 recall: 0.900 combined vs 0.824 numerical/0.725 textual). SHAP analysis provides transparent feature importance rankings, while ablation studies prove textual features' complementary value. This accessible approach offers hospitals a balance of high performance (F1=89%), computational efficiency, and clinical interpretability - addressing critical needs in prostate cancer diagnostics.

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