Optimized Ensembled Model for Patient Treatment Using Machine Learning Techniques

The accurate prediction of whether a patient will require in-care or out-care treatment is a critical challenge in the healthcare industry, the use of machine learning algorithms has become increasingly prevalent in healthcare, here aim is to explore the various machine learning algorithms that have been used for determining whether a patient should receive in-care or out-care treatment. A comprehensive review of the literature has been conducted to identify and summarize the ML algorithms that have been used for this purpose, such as logistic regression, decision trees, support vector machines, random forests, and our proposed ensemble model. Our ensemble model is developed by the ensemble of predictions by Sequential Minimal Optimization and Random Forest that ultimately aids in better accuracy, precision, and recall, We have found that this ensemble model always yield good results compared to an individual model. These ensemble models proved fantastic for all sorts of applications, which demonstrate the great potential of enhancing accuracy and the robustness of prediction that could well help in saving many lives.