Deep Leaning for Malnutrition Detection: Implications for Global Health

Abstract

Malnutrition continues to be a major global health concern, impacting millions of people from various demographic backgrounds. To lessen the grave health effects of malnutrition, early detection and focused intervention are essential. Conventional evaluation techniques, like physical examinations, body weight calculations, and blood testing, are frequently expensive, time-consuming, and unpredictable. This research describes a revolutionary machine learning strategy for successfully diagnosing malnutrition. Specifically, we leverage transfer learning approaches from pre-trained models and employ NumPy for linear algebra, pandas for data processing, and matplotlib for interactive graph plotting. Function approximation, prediction robustness, and classification accuracy are all improved by the ensemble framework. After processing the data set, our model divides it into four groups: normal, stunting, obesity, and wasting. The main goal is to accurately classify malnutrition so that customized therapies may be implemented for each group. By doing this, we hope to lower the risk of death, health issues, and physical disabilities brought on by malnutrition. In summary, our suggested approach marks a substantial development in the efficient identification of malnutrition and the provision of tailored care. Accurate classification is ensured with the incorporation of ensemble learning algorithms, opening the door for focused treatments to enhance patient outcomes. Keywords—Analysis, Machine learning, Ensemble learning, NumPy, Matplotlib, Seaborn, Malnutrition.