In this project, your aim is to leverage machine learning to automatically classify patients' examinations into five distinct classes using as predictors cardiac magnetic resonance radiomics.
Results Analysis: Analyze the results to identify the most important features influencing
model predictions. Compare and contrast the performance of different approaches.
Finally, recommend the most suitable approach, if any, for clinical adoption. What steps
do you think would be necessary to bring this model into real clinical use? Critically
comment on which metrics should be used in this problem and why.
NOTES: likely feature selection and explainable features
Results Analysis: Analyze the results to identify the most important features influencing model predictions. Compare and contrast the performance of different approaches. Finally, recommend the most suitable approach, if any, for clinical adoption. What steps do you think would be necessary to bring this model into real clinical use? Critically comment on which metrics should be used in this problem and why.
NOTES: likely feature selection and explainable features