Patient Health Care Opinion Systems using Ensemble Learning

Abstract

The patients’ experience is considered a dominant reputation in the hospital administration and medical fields. Online patient reviews are recognized as an important criterion for evaluating hospital service quality and performance. The classical approach of evaluating service excellence is often found to be tedious. But with machine learning classifiers and opinion mining techniques the data assessing, and evaluation is made casual and its saves time. Currently, patient satisfaction and quality of service for patients in hospitals plays a major role in health care sector. In this paper a novel Ensemble Model is proposed to Analyze Patient Health Care Opinion Systems. The Classification models are used to classify patients’ feelings as positive, negative, or neutral using a machine learning approach to predict superlative models in data analysis. Ensemble techniques are used to analyze the opinions classified by the model, and the recommendation for health care is analyzed based on sentiment polarity.