Visual Interpretation of Machine Learning Models (Random Forest) for Lung Cancer Risk Classification Using Explainable Artificial Intelligence (SHAP & LIME) - Rosyid, Irwan Fathur; Pramaditya, Himawan (Jurnal Teknik Informatika (Jutif), 2025)
Lung cancer remains one of the most prevalent and burdensome cancers worldwide, with delayed diagnosis being a persistent challenge—particularly in Indonesia, where no national screening program currently exists. In this collaborative study, we aim to develop an interpretable machine learning model for classifying lung cancer risk levels using the Explainable Artificial Intelligence (XAI) approach. The CRISP-DM framework was applied, and the dataset underwent cleaning, feature selection, labeling, and transformation, resulting in 152 valid entries. Tree ensemble algorithms—XGBoost, Random Forest, and LightGBM—were used, with Random Forest achieving the best performance at 97.38% accuracy. SHAP and LIME methods were integrated to provide transparent visual interpretations. A web-based system was developed using Streamlit, incorporating these visualizations and automated narrative summaries generated by a language model to assist non-technical users. A simulated case based on a published pediatric lung cancer report was used to demonstrate its interpretability and illustrate its potential applicability in clinical workflows. The proposed system offers an interpretable and scalable solution for early lung cancer risk classification, which may enhance decision support in primary care and promote trust in AI-assisted diagnostics.