Proposed Intelligent Software System for Healthcare Systems Using Machine Learning

This chapter proposes an intelligent software system for healthcare systems using machine learning for predicting patient readmission to face challenges due to a class imbalance in the classification of patients. Predicting early readmission enables an efficient healthcare system, reducing cost burden, as timely care and admission with doctor availability thereby reduce the chances of severity. Imbalanced learning on healthcare dataset unfortunately predicted inconsistent results. Therefore, to address the problem efficiently, a detailed study from 2014 to 2021 was selected for assessment and analysis purposes. The proposed research work organizes and summarizes the existing published research work based on the research questions defined. For this, we conducted a comprehensive experiment for designing an intelligent system using the effects of imbalanced learning on dataset metrics, type of classifier, input metrics and imbalanced learning method. In this research study, the problem of predicting the risk of readmission was framed as a classification problem, and several available prediction models were developed and evaluated. This work explored a complex, high-dimensional clinical dataset of about 80,000 patient records provided by an Indian hospital toward identifying patient prediction datasets, methods and frameworks. This analysis will help doctors and management make more authentic decisions about a patient’s readmission.