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Background <p>Brain tumors require multimodal therapies such as surgery, radiation treatment, and chemotherapy. There must be individual-level predictors that will influence treatment assignment patterns in the synthetic dataset to make precision medicine for neuro-oncology possible.</p> Objective <p>The objective of this study is to demonstrate an interpretable machine learning (ML) approach for comparing predictors of surgery, radiation therapy, and chemotherapy in brain tumors using a synthetic dataset, with an emphasis on model comparison and SHapley Additive exPlanations (SHAP)-based explainability rather than clinical inference.</p> Methods <p>The Kaggle dataset used in this study is fully synthetic and intended for methodological and educational research. Kaggle's 20,000 brain tumor patient data set was downloaded. Sixteen significant features were preprocessed and used for training nine supervised classifiers. The best performance was found using Gradient Boosting, and it was selected for SHAP analysis. SHAP beeswarm and waterfall plots were employed to interpret both global and local feature contributions.</p> Results <p>The Gradient Boosting model performed superior to all three tasks of treatment prediction—surgery, radiation therapy, and chemotherapy—when compared with five key classification metrics. SHAP analysis identified Tumor_Size, Tumor_Growth_Rate, and Age as the strongest predictors for treatments. For surgery, SHAP waterfall plots indicated that Tumor_Growth_Rate = − 1.694 had the highest negative impact (− 0.15), while Age = 0.612 and Symptom_2 = − 1.347 had positive impacts (+ 0.02 each). For radiation treatment, Location = 1.324 reduced treatment probability (− 0.05), while Tumor_Size had positive impact (+ 0.03) but Tumor_Growth_Rate = − 1.694 had a negative impact (− 0.03). Age = 0.612 was the most significant negative impact (− 0.04) in chemotherapy, with Tumor_Size = − 0.381 (− 0.03) and Family_History = − 1 following closely behind. Tumor_Growth_Rate contributed a small level of enhanced prediction (+ 0.01).</p> Conclusion <p>These findings demonstrate the feasibility of using interpretable machine learning methods to explore treatment-related patterns in healthcare-like data and provide a foundation for future validation using real-world clinical datasets.</p>

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Comparing predictive factors for surgery, radiation treatment, and chemotherapy in brain tumor synthetic records using SHAP values

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月7日

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  • Eyachew Misganew Tegaw,
  • Betelhem Bizuneh Asfaw

摘要

Background

Brain tumors require multimodal therapies such as surgery, radiation treatment, and chemotherapy. There must be individual-level predictors that will influence treatment assignment patterns in the synthetic dataset to make precision medicine for neuro-oncology possible.

Objective

The objective of this study is to demonstrate an interpretable machine learning (ML) approach for comparing predictors of surgery, radiation therapy, and chemotherapy in brain tumors using a synthetic dataset, with an emphasis on model comparison and SHapley Additive exPlanations (SHAP)-based explainability rather than clinical inference.

Methods

The Kaggle dataset used in this study is fully synthetic and intended for methodological and educational research. Kaggle's 20,000 brain tumor patient data set was downloaded. Sixteen significant features were preprocessed and used for training nine supervised classifiers. The best performance was found using Gradient Boosting, and it was selected for SHAP analysis. SHAP beeswarm and waterfall plots were employed to interpret both global and local feature contributions.

Results

The Gradient Boosting model performed superior to all three tasks of treatment prediction—surgery, radiation therapy, and chemotherapy—when compared with five key classification metrics. SHAP analysis identified Tumor_Size, Tumor_Growth_Rate, and Age as the strongest predictors for treatments. For surgery, SHAP waterfall plots indicated that Tumor_Growth_Rate = − 1.694 had the highest negative impact (− 0.15), while Age = 0.612 and Symptom_2 = − 1.347 had positive impacts (+ 0.02 each). For radiation treatment, Location = 1.324 reduced treatment probability (− 0.05), while Tumor_Size had positive impact (+ 0.03) but Tumor_Growth_Rate = − 1.694 had a negative impact (− 0.03). Age = 0.612 was the most significant negative impact (− 0.04) in chemotherapy, with Tumor_Size = − 0.381 (− 0.03) and Family_History = − 1 following closely behind. Tumor_Growth_Rate contributed a small level of enhanced prediction (+ 0.01).

Conclusion

These findings demonstrate the feasibility of using interpretable machine learning methods to explore treatment-related patterns in healthcare-like data and provide a foundation for future validation using real-world clinical datasets.