Using Biological Variables and Social Determinants to Predict Malaria and Anemia among Children in Senegal
This work addresses child mortality from anemia and malaria in Senegal, but it is incremental as it uses existing methods on new data without novel methodological contributions.
The paper tackled predicting malaria and anemia in Senegalese children using biological and social determinants, applying four standard machine learning algorithms to Demographic and Health Survey data, but did not report concrete performance numbers.
Integrating machine learning techniques in healthcare becomes very common nowadays, and it contributes positively to improving clinical care and health decisions planning. Anemia and malaria are two life-threatening diseases in Africa that affect the red blood cells and reduce hemoglobin production. This paper focuses on analyzing child health data in Senegal using four machine learning algorithms in Python: KNN, Random Forests, SVM, and Naïve Bayes. Our task aims to investigate large-scale data from The Demographic and Health Survey (DHS) and to find out hidden information for anemia and malaria. We present two classification models for the two blood disorders using biological variables and social determinants. The findings of this research will contribute to improving child healthcare in Senegal by eradicating anemia and malaria, and decreasing the child mortality rate.