Year: 2024
Type: article
Abstract: Malnutrition remains a significant global health problem, linked to a substantial proportion of child deaths worldwide. According to the United Nations, malnutrition is responsible for 45% of deaths in children under five. The World Food Programme estimates that over 820 million people globally suffer from hunger, with malnutrition playing a crucial role in this crisis. This study uses Python for data analysis and visualization, integrating time-series analysis and deep learning to forecast global malnutrition trends. The system processes data from 1970 to 2022, normalizes it, and trains a model comprising Conv1D and LSTM layers. The predictions are visualized using Plotly and displayed in a Flask web application, offering interactive features for exploring the data. The results highlight a notable decline in malnutrition-related deaths in both developing and developed nations, reflecting the success of previous interventions. However, developing countries continue to report a higher number of diseases and conditions associated with malnutrition, underscoring the need for further targeted interventions.
Authors: Mia Karisma Haq, Ihsan Ghozi Zulfikar, Rhisma Syahrul Putra, Mohamad Azfar Syazani Bin Md Yusof, Arianti Apriani Sagita, Della Rachmatika Noer Intanty
Institutions: Indonesia University of Education, Universiti Teknologi MARA
Language: English
Subfield: Nutrition and Dietetics
Field: Nursing
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4407109280