Developing Model-Agnostic Meta-Learning Enabled Lightbgm Model Asthma Level Prediction in Smart Healthcare Modeling

Abstract

Millions of people across the world suffer from the chronic respiratory condition known as asthma. Predicting the severity of asthma based on a variety of personal and environmental characteristics might yield useful information for preventative measures. LightGBM model is a gradient-boosted model with the potential for great accuracy, but it requires careful hyperparameter adjustment to reach its full potential. Common tuning techniques have a hard time generalizing to new data distributions. The dataset was used, and its many subsets were used to represent various demographics and geographic areas. LightGBM was configured with hyperparameters, trained on a sample dataset, and then verified for each job. To quickly adjust to new tasks, the MAML method sought to find the optimal values for its hyperparameters. After the meta-training step was complete, the generalizability of the hyperparameters was tested on new data. After including MAML for hyperparameter adjustment, the Light- GBM model showed a gain of 7% in accuracy, coming in at 98.5%. Predictions of severe asthma had a crucially high 97.8 percent degree of accuracy. The model’s recall rate for severe asthma levels was 97.4%, demonstrating its capacity to reliably detect and anticipate important instances. An F1-score of 97.1%, a metric that averages the accuracy and recall of a model, is indicative of good overall performance.. For gradient-boosted model applications like asthma level prediction, MAML provides a viable path for hyperparameter adjustment. Although there are obstacles to be overcome, this method has the potential to greatly improve the flexibility and precision of predictive healthcare models. More effective implementations and a wider range of applications can be explored in future studies.