What is it about?
This study looked at how to make machine learning models more transparent and reliable when predicting heart disease in patients. Machine learning models are used to analyze large amounts of data and make predictions about health outcomes, but it can be hard to tell if these models are accurate and unbiased. The researchers used a technique called bootstrap simulation to test how accurate the machine learning models were, and they also used a method called SHAP to explain how the models made their predictions. By doing this, they were able to identify which machine learning model was the most accurate and which features were the most important in predicting heart disease. This study is important because it helps researchers and doctors understand how machine learning models work and how they can be improved to make better predictions about patient health.
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Why is it important?
The study contributes to the literature by providing a comprehensive framework for applying machine learning in medical applications. It includes an initial machine learning selection methodology that uses bootstrap simulation to compute confidence intervals of numerous model accuracy statistics, a feature selection methodology that incorporates multiple feature importance statistics, and a way to accurately visualize clinically relevant features using SHAP. This approach can increase the transparency and reliability of machine learning methods in medical applications and help clinicians identify the best model for a given dataset. Additionally, the study shows that XGBoost is the most accurate model for predicting heart disease in the England National Health Services Heart Disease Prediction Cohort.
Read the Original
This page is a summary of: Increasing transparency in machine learning through bootstrap simulation and shapely additive explanations, PLoS ONE, February 2023, PLOS, DOI: 10.1371/journal.pone.0281922.
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Shapley Additive Explanations (SHAP)
In this video you'll learn a bit more about: - A detailed and visual explanation of the mathematical foundations that comes from the Shapley Values problem; - How does SHAP (Shapley Additive Explanations) reframes the Shapey Value problem - What is Local Accuracy, Missingness, and Consistency in the context of explainable models - What is the Shapley Kernel - An example that shows how a prediction can be examined using Kernel SHAP. Author: Rob Geada - e-mail: firstname.lastname@example.org - LinkedIn: https://www.linkedin.com/in/rob-geada... 00:00 Introduction 00:23 Shapley Values 02:27 Shapley Additive Explanations 04:16 Local Accuracy, Missingness, and Consistency 05:58 Shapley Kernel 08:06 Example
Gradient boosting is a machine learning technique used in regression and classification tasks, among others. It creates a prediction model by combining weak prediction models, typically decision trees, in an ensemble. The resulting algorithm is called gradient-boosted trees, which often outperforms random forests. Gradient boosting builds the model in a stage-wise manner, optimizing an arbitrary differentiable loss function.
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