Data Mining Techniques for Predictive Classification of Anemia DiseaseSubtypes

Data Mining Techniques for Predictive Classification of Anemia DiseaseSubtypes

anemia; data mining; J48 decision tree; naïve bayes; random forest

Anemia, characterizedby insufficient red blood cells or reduced hemoglobin,hinders oxygen transport in the body. Understanding thevarioustypes of anemiais vital to tailor effective prevention and treatment. This research explores data mining's role in predicting and classifying anemia types, emphasizingComplete Blood Count (CBC) and demographic data. Data mining is key to building models that aidhealthcare professionals in thediagnosis 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 J48Decision Tree outperformed the others, highlighting the importance ofalgorithm choicein anemia classification models. Furthermore,our analysis identified renal disease-related and chronic anemia as the most prevalent types, with ahigher incidenceamong women.Recognizinggender disparities in the prevalence ofanemiainforms personalizedhealthcare decisions. Understanding demographic factors in specific types ofanemiais crucial for effective care strategies.

Johan Setiawan1, Dita Amalia2, Iwan Prasetiawan3

https://jurnal.iaii.or.id/index.php/RESTI/article/view/5445/887

Department of Information Systems, Faculty of Engineering and Informatics, Universitas Multimedia Nusantara, Tangerang, Indonesia

15-01-2024

FAJAR BAGUS W

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