Analysis of Malnutrition Prediction in Children using ML Techniques

Malnutrition remains one of the main reasons why young children die and get sick, significantly impacting their growth and development. To avoid long-term health effects, early detection of malnutrition is essential, and advanced technologies such as deep learning (DL) and machine learning (ML) provide promising solutions in predicting malnutrition risk. This research investigates the possibilities of ML and DL models in accurately predicting malnutrition in young children by analyzing a wide array of demographic, nutritional, and health-related factors. By employing advanced data analytics techniques, the research seeks to identify key risk factors and develop predictive models It can be used in environments with low resources. The study applies a range of Convolutional and recurrent neural networks are examples of deep learning designs; support vector machines, random forests, and decision trees, and makes use of several datasets, including medical history and dietary intake records. The efficacy of these models is assessed based on model robustness, forecast accuracy, and potential for real-world application. The findings indicate that these models offer significant promise for early diagnosis of malnutrition, enabling targeted interventions that can improve health outcomes for vulnerable populations.