A Novel Support Vector Machine based Improved Aquila Optimizer-based Text Mining Mechanism for the Healthcare Applications

Abstract

Social media acts as one of the biggest contributions in every field. In healthcare applications it helps to estimate the quality of the services provided by different hospitals and doctors. Using the text mining technique, the services are analyzed. Several text mining techniques were performed in recent times. However, the effectiveness of text mining in the healthcare field is still a complicated task. Hence, we propose a novel Support Vector Machine (SVM) based Improved Aquilla Optimizer (IAO) algorithm to enhance the text mining from the reviews in the social media. Using this patient can easily evaluate the quality and services of particular clinics and doctors. The work includes the preprocessing of the dataset collected and then discriminative least square regression (DLSR) for the extraction of features from the preprocessed data. Experimental analysis is conducted to analyze the performance of the proposed work. The results are compared with state-of-art works with different performance metrics. Thus, our proposed work can be used to mine the text for the healthcare applications.