GitHub
explainable_heart_disease_prediction_using_ensemble-quantum_ml
An ensemble machine learning model based on quantum machine learning classifiers is proposed to predict the risk of heart disease. The proposed model is a bagging ensemble learning model where Quantum Support Vector Classifier is used as the base classifier. Furthermore, in order to make the model's outcomes more explainable, the importance of every single feature in the prediction is computed and visualized using SHapley Additive exPlanations (SHAP) framework. In the experimental study, other stand-alone quantum classifiers, namely, Quantum Support Vector Classifier (QSVC), Quantum Neural Network (QNN), and Variational Quantum Classifier (VQC) were applied and compared with classical machine learning classifiers such as Support Vector Classifier (SVC), and Artificial Neural Network (ANN).
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A Bayesian adaptive psychometric method for measuring thresholds in online experiments.
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C++ software toolbox to support building spatial design optimization developed at the Eindhoven University of Technology
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This repo is a demo which contains the core of what every repo should contain, a template. use https://github.com/OpenScienceMOOC/Module-5-Open-Research-Software-and-Open-Source/blob/master/content_development/Task_1.md for inspiration
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This is a read-only mirror of the git repos at https://bioconductor.org
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Solve the (ro)vibrational Schödinger Equation with a basis of B-splines, optionally with a complex absorbing potential
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2024-02_pbe_vbsa
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iris
Iris - P2P System for Confidential Sharing of Threat Intelligence and Collaborative Defense for Slips