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.


Machine learning has the potential to make a positive impact in healthcare by working synergistically with clinical judgment that physicians have developed over time. By utilizing large datasets and complex algorithms, machine learning can identify patterns and predict outcomes that may not be immediately apparent to a human clinician. This can lead to more accurate diagnoses and treatment plans, as well as early detection of potential health issues. However, it is important to note that machine learning algorithms should not be relied upon exclusively, but rather as a tool to aid in clinical decision-making. Physicians and other healthcare professionals play a critical role in interpreting and contextualizing the results provided by machine learning algorithms to ensure that the best possible care is provided to each individual patient. Therefore, when machine learning is used in conjunction with clinical judgment, it has the potential to revolutionize healthcare and improve patient outcomes.

Dr. Samuel Y Huang
Mount Sinai Health System

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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