Unveiling Insights from Hematobiometry Data: A Data Science Approach Using Data from a Quito Clinical Laboratory

In this applied research study, a data science approach is employed to analyze anonymized hematological data obtained from a clinical laboratory located in Quito, Ecuador. The analysis aims to examine machine learning models that could potentially be used to aid in early anemia and polycythemia detection, ultimately contributing to improved healthcare decision-making. A rigorous MLOps-driven methodology is employed, and well-established techniques such as clustering, decision trees, and neural networks are applied. These methods are evaluated to identify the most suitable approach for the specific characteristics of the data. The findings showed that clustering methods were not advisable for the type of data used for the exploration and no significative results could be obtained. However, decision trees and neural networks demonstrated superior performance in predicting the presence of these blood disorders. Additionally, the outcomes of this research have the potential to be particularly significant for Ecuador, a nation facing challenges in healthcare access and malnutrition, where early anemia detection could be highly impactful.