Reliable Machine Learning Classifiers and Big Data Sentiment Analysis for Evaluating the Patient Health Care Opinion Systems

Abstract

The patients experience is considered a dominant reputation in the hospital newlineadministration and medical fields. Online patient reviews are recognized as an important newlinecriterion for evaluating hospital service quality and performance. The classical approach of newlineevaluating service excellence is often found to be tedious. But with machine learning newlineclassifiers and opinion mining techniques the data assessing, and evaluation is made casual newlineand its saves time. Currently, patient satisfaction and quality of service for patients in newlinehospitals plays a major role in health care sector. This is accomplished by predicting the newlinevaried hidden patterns along with identifying the key components responsible for patient newlinesatisfaction. This research work tried to ensure patient satisfaction and quality of service newlineby proposing Machine Learning Classifiers and Big Data Analytics for Evaluating the newlinePatient Health Care Opinion Systems. newlineFirstly, for doing sentiment analysis in patient service satisfaction, a systematic newlineoverview is presented which focuses on review comments and opinion polls related to the newlinehealth sector in quality service, characteristics associated with patient satisfaction, newlinecomments on drug reviews and recommendations. newlineSecondly, this proposed work provides novel SCSP Ensemble Model to Analyze newlinePatient Health Care Opinion Systems. The Classification models are used to classify newlinepatients feelings as positive, negative, or neutral using a machine learning approach to newlinepredict superlative models in data analysis. Ensemble techniques are used to analyze the newlineopinions classified by the model, and the recommendation for health care is analyzed based newlineon sentiment polarity. The very reason behind selecting this topic is to provide a sound newlineinformation system to the healthcare industry based on the tweets posted. newlineThen this research work provides the novel feature selection method for identifying newlinethe key feature in Home Health Care Services using patient satisfaction data. In this, we newlineexamined the several components to e