Cross-Validation in AI and Machine Learning for Healthcare

Every time we create a machine learning model, we feed it with data to train it. Then we give the model some unlabeled data (test data) to check how well it performs and generalizes to new data. This model is stable if it works well on anonymous data, is consistent, and can forecast with high accuracy on a wide range of input data.

But, this isn’t always the case! Machine learning models are not always stable; Thus, we must assess their stability. Cross-Validation enters the scene at this point.

Cross-validation in machine learning is a methodology in which we teach our model on a part of the data set and then evaluate it on the remaining computer vision dataset.

The following are the three steps involved in cross-validation:

  • A subset of the sample data set should be kept aside.
  • Train the model using the rest of the data.
  • Use the data-reserve set’s component to test the model.

Cross-Validation Techniques


Half of the data set is utilized for training in this procedure, while the other half is used for testing. The most significant disadvantage of this method is that we only train on half of the dataset; The remaining 50% of the data likely contains critical information that we overlook when preparing our model, resulting in more considerable bias.

LOOCV (Leave One Out Cross Validation)

This approach iterates for each data point by training the whole dataset but leaving just one data point from the accessible dataset. It has several benefits as well as some drawbacks.

This strategy benefits making use of all data points, resulting in reduced bias.

Because we are testing against a single data point, this strategy has the primary disadvantage of causing more variance in the testing model. If a data point is an outlier, the variation will be greater. Another disadvantage is that it consumes significant processing time because it iterates over ‘the number of data points’ times.

Cross-Validation Using K-Folds

This approach divides the data set into k subsets (also known as folds), then trains all subsets while leaving one (k-1) subset for assessment of the trained model. We iterate k times with a distinct subgroup reserved for testing purposes each time in this procedure.

Why Do you Require Cross-Validation?

Assume you’ve created a machine learning model to tackle a problem, and you’ve trained it on a set of data. When you look at the model’s accuracy on the training data, it’s close to 95%. Is this to say that your model has trained very effectively and is the top due to its high accuracy?

No, it isn’t! Because your model was trained on the provided data, it is pretty familiar with it, has caught even minor deviations (noise), and has generalized well over it. When the model is exposed to wholly new, previously unseen data, it may not predict as well and may fail to generalize over the latest data. Over-fitting is the term for this issue.

Because it cannot discover patterns, the model does not always train effectively on the training set. It would not do well on the test set in this situation. Under-fitting is the term for this issue.

Cross-Validation in the Healthcare Sector

Clinicians naturally wonder how they can trust the results and suggestions of AI and ML models when they enter clinical practice. There have been healthcare data breaches, including data from radiological imaging databases, further complicating problems. Our AI models or the data used to train them in danger?

Physician confidence in clinical models’ accuracy and efficacy is critical. Without it, these tools will struggle to acquire traction.

One of four aims for AI in healthcare Research in medical imaging was to develop methods for assessing and measuring the progress of AI algorithms in clinical practice in order to expedite regulatory approval.

Two tests are used to validate the model:

  • Is the model learning from the training data correctly?
  • Is it possible to generalize the final model? Is it possible to use it with data that is comparable but unknown?

Tips and Best Practices

1. When separating the data, be logical (does the splitting method make sense).

2. Employ the correct CV format (is this method viable for my use case).

3. Don’t validate on the past while working with time series (see the first tip).

4. When interacting with medical data, remember to split data by individual. It’s advisable to avoid using data for a single person in the training and test sets because this might be considered a data breach.

5. Image sorting is crucial when cropping patches from larger photos.


Leave a Comment