Health Care Prediction
Keywords:
Machine learning, LGBM, Random Forests, detection.Abstract
This study's goal was to create a patient discharge roster using machine learning methods. The discharge forecasting model suggested was then verified using the risk-based evaluation indicators from the ML tools. The study on a composed health data set of actual data, which includes aspects of severity, department, etc., We created a machine learning model to predict a patient's utilising the model types: Logistic Regression, Random Forests, and Light Gradient Boosting with the optimal parameters with various feature combinations and pre-processing techniques. When compared to all other systems, the suggested model was more accurate. The suggested model has improved accuracy when compared to SVM and NN models.
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