Evaluating Optimizable Machine Learning Models for Anemia Type Prediction from Complete Blood Count Data
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Keywords:
Data Exploration, Machine Learning, Multiclass Classification, Anemia Types, Complete Blood Count, CBC TestAbstract
This paper compares different optimizable machine learning classification models to predict
eight types of anemia from complete blood count (CBC) data. For the research, we used a publicly
available Kaggle dataset containing 1281 observations, 14 predictors, and the diagnosis as the categorical
target variable with nine categories (eight types of anemia and the healthy category). First, we examined
the dataset and observed the histograms of some of the predictors. We compared the values of predictors
of observations with no anemia to the observations where any anemia was diagnosed. Next, we used
MATLAB R2024a to train and test nine optimizable machine-learning classification models. These
models were Ensemble, Tree, SVM, Efficient Linear, Neural Network, Kernel, KNN, Naïve Bayes, and
the Discriminant. Bayesian optimization was used to optimize the hyperparameters of all these models.
We used 90% of observations for training and 10% of observations for testing. During the training, 10
fold cross-validation was used to prevent overfitting. The results showed the best accuracy was reached
with the Ensemble classification model using the bag ensemble method (validation accuracy: 99.22%, test
accuracy: 100%). Finally, we inspected our best classification model in more detail. We calculated the
permutation feature importance to determine the contribution of each predictor to the final model. The
results showed 6–7 important predictors, while the most important feature was the amount of hemoglobin.
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