Abstract:
Anemia is a public health disease that reflects the lack of red blood cells or hemoglobin to carry the requisite amount of oxygen to the body, causing weakness, fatigue, and diminished cognition. It is a gigantic public health concern in developing countries like Bangladesh, where the timely diagnosis is still hampered because of less availability of resources and low awareness amongst common people. The study begins to create an intelligent and accurate anemia prediction model using various machine learning models with patient data from a Bangladesh general hospital. The methodology involved a systematic preprocessing of the dataset, including dealing with missing values, normalization, and categorical encoding, and splitting the dataset into training data and test data. Ten historical classifiers were utilized: Random Forest (RF), Logistic Regression (LR), Gaussian Naïve Bayes (GNB), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Quadratic Discriminant Analysis (QDA), Ridge Classifier (RC), Passive Aggressive (PA), XGBoost (XGB), and Grid Search CV (GS). Bagging, boosting, and voting were carried out to ensemble and improve classifiers. Accuracy, precision, recall, and F1 score were used as the measurement metrics. According to the findings, the Voting classifier performed better than all others in performance with the highest accuracy of 93.12%, followed by KNN, GS, and RF under bagging and the default setting. Boosting generally delivered a mixed performance with overfitting shortening some of the models. The study concludes.
