Leveraging Shapley Additive Explanations for Feature Selection in Ensemble Models for Diabetes Prediction [PDF]
BioengineeringDiabetes, a significant global health crisis, is primarily driven in India by unhealthy diets and sedentary lifestyles, with rapid urbanization amplifying these effects through convenience-oriented living and limited physical activity opportunities ...
Prasant Kumar Mohanty +4 more
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Explaining Deep Q-Learning Experience Replay with SHapley Additive exPlanations
Machine Learning and Knowledge Extraction, 2023 Reinforcement Learning (RL) has shown promise in optimizing complex control and decision-making processes but Deep Reinforcement Learning (DRL) lacks interpretability, limiting its adoption in regulated sectors like manufacturing, finance, and healthcare.
Robert S. Sullivan, Luca Longo
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Leveraging SHapley Additive exPlanations (SHAP) and fuzzy logic for efficient rainfall forecasts [PDF]
Scientific ReportsThe precision of rainfall forecasts remains a critical concern for meteorological services, as accurate forecasts enable governments and communities to prepare for floods, droughts, and water scarcity crises.
Seyed Matin Malakouti
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Big Data and Cognitive Computing, 2022 Artificial intelligence (AI) and machine learning (ML) models have become essential tools used in many critical systems to make significant decisions; the decisions taken by these models need to be trusted and explained on many occasions.
Remah Younisse +2 more
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Predicting and Interpreting Student Performance Using Ensemble Models and Shapley Additive Explanations [PDF]
IEEE Access, 2021 In several areas, including education, the use of machine learning, such as artificial neural networks, has resulted in significant improvements in predicting tasks. The opacity of these models is one of the problems with their use.
Hayat Sahlaoui +4 more
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Shapley additive explanations for NO2 forecasting
Ecological Informatics, 2020 Abstract In this paper, we address the problem of the interpretability of a machine learning model designed to predict air quality time series. When constructing a forecasting model, in addition to obtaining good accuracy, it is utterly important to understand why each prediction is made. Usually, interpreting the output of machine learning models is
JOSÉ L Aznarte
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Caffeine and Beetroot Juice Optimize 1,000-m Performance: Shapley Additive Explanations Analysis [PDF]
American Journal of Men's HealthThe 1,000-m run is a key component of university physical fitness assessments. Effective supplementation strategies to enhance performance and recovery in this test remain underexplored.
Xiao Liu +5 more
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Journal of Imaging, 2022 Dengue is a viral disease that primarily affects tropical and subtropical regions and is especially prevalent in South-East Asia. This mosquito-borne disease sometimes triggers nationwide epidemics, which results in a large number of fatalities.
Shihab Uddin Chowdhury +5 more
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Detection of Monkeypox Cases Based on Symptoms Using XGBoost and Shapley Additive Explanations Methods [PDF]
Diagnostics, 2023 The monkeypox virus poses a novel public health risk that might quickly escalate into a worldwide epidemic. Machine learning (ML) has recently shown much promise in diagnosing diseases like cancer, finding tumor cells, and finding COVID-19 patients.
Alireza Farzipour +2 more
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Dual-radiomics based on SHapley additive explanations for predicting hematologic toxicity in concurrent chemoradiotherapy patients [PDF]
Discover OncologyBackground This study investigates the application of a machine learning model that integrates radiomic features and dosiomic features to predict hematologic toxicity (HT) in patients with advanced cervical cancer undergoing concurrent chemoradiotherapy (
Luqiao Chen +7 more
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