Data Mining Techniques for Predictive Classification of Anemia Disease Subtypes

Setiawan, Johan and Amalia, Dita and Prasetiawan, Iwan (2024) Data Mining Techniques for Predictive Classification of Anemia Disease Subtypes. Data Mining Techniques for Predictive Classification of Anemia Disease Subtypes, 8 (1). pp. 10-17. ISSN 2580-0760

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Abstract

Anemia, characterized by insufficient red blood cells or reduced hemoglobin, hinders oxygen transport in the body. Understanding anemia's diverse types is vital to tailor effective prevention and treatment. This research explores data mining's role in predicting and classifying anemia types, emphasising Complete Blood Count (CBC) and demographic data. Data mining is key to building models aiding healthcare professionals in anemia diagnosis and treatment. Employing the Cross-Industry Standard Process for Data Mining (CRISP-DM), with its six phases, facilitates this endeavor. Our study compared Naïve Bayes, J48 Decision Tree, and Random Forest algorithms using RapidMiner's tools, evaluating accuracy, mean recall, and mean precision. J48 Decision Tree outperformed the others, highlighting algorithm choice's significance in anemia classification models. Moreover, our analysis identified renal disease-related and chronic anemia as the most prevalent types, with higher occurrences among females. Recognizing gender disparities in anemia's prevalence informs tailored healthcare decisions. Understanding demographic factors in specific anemia types is crucial for effective care strategies. Keywords: anemia; data mining; J48 decision tree; naïve bayes; random forest

Item Type:	Article
Keywords:	anemia; data mining; J48 decision tree; naïve bayes; random forest
Subjects:	000 Computer Science, Information and General Works > 000 Computer Science, Knowledge and Systems > 000 Computer Science, Information and General Works 000 Computer Science, Information and General Works > 000 Computer Science, Knowledge and Systems > 001 Knowledge > 001.4 Research
Divisions:	Faculty of Engineering & Informatics > Information System
Depositing User:	Iwan Prasetiawan (L00552)
Date Deposited:	20 Mar 2024 03:49
Last Modified:	20 Mar 2024 03:49
URI:	https://kc.umn.ac.id/id/eprint/29694

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