2024
DOI: 10.32628/ijsrset24113134
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Random Forest and CatBoost with Handling Imbalanced Class for Detection of Risk Factors Anemia in Children (5-12 Years)

Ditia Yosmita Praptiwi,
Anang Kurnia,
Anwar Fitrianto
et al.

Abstract: The prevalence of anemia in children (5-12 years) remains a public health issue in Indonesia. Early detection and control of risk factors are crucial for prevention. Machine learning models can be employed to address this problem. One practical approach is using ensemble learning models. However, it is expected to encounter imbalanced class problems when analyzing health data. Therefore, this study aims to perform classification modeling using two ensemble learning models: Random Forest (RF) and CatBoost. The … Show more

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