AutoML frameworks for disease prediction using medical claims

In healthcare, accurate illness prediction is essential for early discovery, prompt treatment, and better patient outcomes. The quantity of data from medical claims offers a chance to use machine learning methods to anticipate diseases. The complicated and unstructured nature of medical claims data makes it difficult to create precise prediction models. The emergence of AutoML frameworks as potent tools for tackling the difficulties of illness prediction using medical claims is presented in this research. We examine and evaluate several cuttingedge AutoML frameworks and their use in illness prediction tasks with data from medical claims. These frameworks use cutting-edge algorithms and approaches, including feature engineering, ensemble learning, and model stacking, to improve illness prediction models' predictive accuracy and interpretability. A broader spectrum of medical professionals and researchers may use AutoML because of its automated nature, which facilitates the effective use of computing resources and lessens the load of domain expertise. This study uses data from medical claims to demonstrate the potential of AutoML frameworks for precise illness prediction. In order to fully realise the promise of medical claims data for better patient care and health outcomes, it emphasises the significance of automated techniques in healthcare analytics and the necessity of more research and development in this field.