Interpretable machine learning with tree-based shapley additive explanations: Application to metabolomics datasets for binary classification. [PDF]
PLoS ONE, 2023 Machine learning (ML) models are used in clinical metabolomics studies most notably for biomarker discoveries, to identify metabolites that discriminate between a case and control group.
Olatomiwa O Bifarin
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A Machine Learning Risk Prediction Model for Gastric Cancer with SHapley Additive exPlanations. [PDF]
Cancer Res TreatPurposeGastric cancer (GC) prediction models hold potential for enhancing early detection by enabling the identification of high-risk individuals, facilitating personalized risk-based screening, and optimizing the allocation of healthcare resources. Materials and MethodsIn this study, we developed a machine learning-based GC prediction model utilizing ...
Park B +6 more
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Integrating Machine Learning and the SHapley Additive exPlanations (SHAP) Framework to Predict Lymph Node Metastasis in Gastric Cancer Patients Based on Inflammation Indices and Peripheral Lymphocyte Subpopulations [PDF]
Journal of Inflammation ResearchZiyu Zhu,1,* Cong Wang,1,* Lei Shi,2 Mengya Li,3 Jiaqi Li,3 Shiyin Liang,3 Zhidong Yin,1 Yingwei Xue1 1Department of Gastroenterological Surgery, Harbin Medical University Cancer Hospital, Harbin, People’s Republic of China; 2Department of ...
Zhu Z +7 more
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Counterfactual Shapley Additive Explanations
2022 ACM Conference on Fairness Accountability and Transparency, 2022 Feature attributions are a common paradigm for model explanations due to their simplicity in assigning a single numeric score for each input feature to a model. In the actionable recourse setting, wherein the goal of the explanations is to improve outcomes for model consumers, it is often unclear how feature attributions should be correctly used.
Emanuele Albini +3 more
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Explanation of machine learning models using shapley additive explanation and application for real data in hospital [PDF]
Computer Methods and Programs in Biomedicine, 2022 When using machine learning techniques in decision-making processes, the interpretability of the models is important. In the present paper, we adopted the Shapley additive explanation (SHAP), which is based on fair profit allocation among many stakeholders depending on their contribution, for interpreting a gradient-boosting decision tree model using ...
Yasunobu Nohara +3 more
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Illuminating the Neural Landscape of Pilot Mental States: A Convolutional Neural Network Approach with Shapley Additive Explanations Interpretability [PDF]
Sensors, 2023 Desmond Bala Bisandu +2 more
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Interpretable machine learning in damage detection using Shapley Additive Explanations [PDF]
ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering, 2021 In recent years, Machine Learning (ML) techniques have gained popularity in Structural Health Monitoring (SHM). These have been particularly used for damage detection in a wide range of engineering applications such as wind turbine blades. The outcomes of previous research studies in this area have demonstrated the capabilities of ML for robust damage ...
Tcherniak, Dmitri +2 more
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Measuring Model Understandability by means of Shapley Additive Explanations
2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2022 In this work we link the understandability of machine learning models to the complexity of their SHapley Additive exPlanations (SHAP). Thanks to this reframing we introduce two novel metrics for understandability: SHAP Length and SHAP Interaction Length. These are model-agnostic, efficient, intuitive and theoretically grounded metrics that are anchored
Ettore Mariotti +2 more
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Exact Shapley values for local and model-true explanations of decision tree ensembles
Machine Learning with Applications, 2022 Additive feature explanations using Shapley values have become popular for providing transparency into the relative importance of each feature to an individual prediction of a machine learning model. While Shapley values provide a unique additive feature
Thomas W. Campbell +3 more
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Shapley Additive Explanations of Indicator PCB-138 Distribution in Breast Milk [PDF]
Proceedings of the International Scientific Conference - Sinteza 2020, 2020 Breastfeeding provides numerous health benefits for newborns by meeting the infantsʼ nutritional needs and supporting associated immunological protection. Maternal milk is high in fat, and therefore, represents a very suitable media for the bioaccumulation of lipophilic pollutants such as organochlorine pesticides (OCPs) and polychlorinated biphenyls ...
Stojić, Andreja +2 more
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