Sherin Thomas, Data Scientist
Artificial IntelligenceData ScienceHealthcare TechnologiesMachine Learning

Understanding Class Imbalance in Machine Learning: A Healthcare Perspective

cxomagazine
By Sherin Thomas, Data Scientist

Machine learning (ML) is all about learning from past data to make predictions. We train models on existing data and let it learn patterns on its own. Once trained, the model can make predictions on new data that it has never seen before.

Most machine learning problems fall into two main categories:

  • Classification: The goal is to predict which category or label a data point belongs to. For example, predicting whether a patient has diabetes or not.
  • Regression: The goal is to predict a continuous value.

For example, predicting a patient’s blood sugar level.

Class imbalance is a common problem in real world machine learning, especially in healthcare. If it is ignored, models may appear to perform well but fail when it truly matters.

Machine Learning in Healthcare

Machine learning has become especially valuable in healthcare. For instance, utilizing characteristics like blood pressure, insulin, BMI, glucose level, and more, models can be trained to determine if a patient has diabetes. A well-known dataset used for this task is the Pima Indians Diabetes dataset

Recently, I worked on a Freezing of Gait (FoG) analysis project. FoG is a condition commonly seen in patients with Parkinson’s disease, where patients suddenly freeze while walking for short or long periods. My goal was to build a machine learning model that could predict FoG events using data from the Daphne FoG dataset.

The Challenge I Kept Seeing: Class Imbalance

While working on this project, I encountered a problem that I have seen repeatedly in many machine learning projects: class imbalance. In the FoG dataset, only about 9.5% of the data points corresponded to actual FoG events, while the remaining data represented normal walking. As a result, the dataset was heavily skewed toward one class, with very limited representation of the other.

Because this issue has appeared consistently across my projects, I wanted to spend some time understanding this issue more deeply and the strategies required to address it effectively.

What Is Class Imbalance?

Class imbalance happens when one class has many more examples than the other classes.

  • In classification problems, this might mean that 95% of the data belongs to one class, and only 5% belongs to the other.
  • In regression problems, imbalance can appear as rare extreme values that are very different from most of the data.

In simple terms, the model sees the majority class much more often. It consequently excels in that class and finds it difficult to learn in the minority class.

Why Is Class Imbalance a Serious Problem? Class imbalance causes several issues:

  • The model does not see enough examples of the minority class to learn useful patterns.
  • In healthcare, rare cases are often the most important ones.
  • Getting more data for rare medical conditions is often expensive, slow, or impossible.
  • Model accuracy can give a false sense of confidence.

For example, imagine a dataset where 95% of patients do not have cancer. A model could achieve 95% accuracy by always predicting “no cancer.” However, this model would completely fail to identify actual cancer patients.

In healthcare, this is dangerous. Missing a serious condition is usually far worse than raising a false alarm.

How Can We Handle Class Imbalance?

There is no single solution, but several techniques can help.

Oversampling

Oversampling means increasing the number of minority class samples. This can be done by duplicating existing samples or by creating new synthetic samples. The goal is to give the model more examples of the rare class.

Undersampling

Undersampling means reducing the number of majority class samples. This helps balance the dataset, but it can also remove useful information if too much data is discarded.

Class Weighting

Class weighting changes how the model learns. Misclassifications made on the minority class are given a higher penalty during training. This forces the model to pay more attention to rare cases.

Each of these methods has strengths and weaknesses. The best choice depends on the dataset and the problem.

Why Evaluation Metrics Matter So Much

When working with imbalanced data, using the right evaluation metrics is extremely important.

  • Accuracy only measures how many predictions are correct overall. It does not tell us how well the model performs on rare cases.
  • Precision tells us how many predicted positive cases were actually correct.
  • Recall tells us how many actual positive cases the model was able to find.
  • F1-score balances precision and recall into a single number.

These metrics give a much clearer picture of model performance when classes are imbalanced.

Advanced Metrics Used in Healthcare

I read this paper published in The Lancet journal that discussed better ways to evaluate machine learning models in healthcare. The paper introduced several important metrics:

  • AUROC (C-statistic): Measures how well the model separates different classes.
  • Calibration plots: Show how well predicted probabilities match real outcomes.
  • Risk distribution plots: Show how risk scores are spread across patients.
  • Net Benefit (NB) with decision curves: Helps evaluate clinical usefulness.
  • Expected Cost (EC) with cost curves: Shows the trade-off between different types of errors.

These metrics focus not just on accuracy, but on real-world impact and decision-making.

Final Thoughts

Class imbalance is a common problem in real world machine learning, especially in healthcare. If it is ignored, models may appear to perform well but fail when it truly matters. By understanding class imbalance, applying the right techniques, and using the right evaluation metrics, we can build machine learning models that are more reliable, safer, and more useful in real clinical settings.