Data Mining Techniques for Predictive Classification of Anemia Disease Subtypes

Abstract

Anemia, characterized by insufficient red blood cells or reduced hemoglobin, hinders oxygen transport in the body. Understanding the various types of anemia is vital to tailor effective prevention and treatment. This research explores data mining's role in predicting and classifying anemia types, emphasizing Complete Blood Count (CBC) and demographic data. Data mining is key to building models that aid healthcare professionals in the diagnosis and treatment of anemia. Employing the Cross-Industry Standard Process for Data Mining (CRISP-DM), with its six phases, facilitates this endeavour. Our study compared Naïve Bayes, J48 Decision Tree, and Random Forest algorithms using RapidMiner's tools, evaluating accuracy, mean recall, and mean precision. The J48 Decision Tree outperformed the others, highlighting the importance of algorithm choice in anemia classification models. Furthermore, our analysis identified renal disease-related and chronic anemia as the most prevalent types, with a higher incidence among women. Recognizing gender disparities in the prevalence of anemia informs personalized healthcare decisions. Understanding demographic factors in specific types of anemia is crucial for effective care strategies.

Keywords

• anemia
• data mining
• j48 decision tree
• naive bayes
• random forest