Research Article An Artificial Intelligence-Based Reactive Health Care System for Emotion Detections Gouse Baig Mohammad , 1 Sirisha Potluri, 2 Ashwani Kumar , 3 Ravi Kumar A, 4 Dileep P, 5 Rajesh Tiwari, 6 Rajeev Shrivastava, 7 Sheo Kumar, 8 K. Srihari , 9 and Kenenisa Dekeba 10 1 Department of Computer Science and Engineering, Vardhaman College of Engineering, Hyderabad, India 2 Department of CSE, Faculty of Science and Technology-IcfaiTech, e ICFAI Foundation for Higher Education, Donthanapally, Shankarpalli Road, Hyderabad, Telangana 501203, India 3 Head Department of CSE (AIML) and Professor, Sreyas Institute of Engineering and Technology, Hyderabad, India 4 Department of Computer Science Engineering, Sridevi Women’s Engineering College, Gandipet, India 5 Department of Computer Science and Engineering, Malla Reddy College of Engineering and Technology, Kompally, Hyderabad, India 6 CMR Engineering College, Hyderabad, India 7 Department of ECE, Princeton Institute of Engineering and Technology for Women, Hyderabad, India 8 CMR Engineering College, Hydrabad, India 9 Department of Computer Science and Engineering, SNS College of Technology, Coimbatore, India 10 Department of Food Process Engineering, College of Engineering and Technology, Wolkite University, Wolkite, Ethiopia Correspondence should be addressed to K. Srihari; harionto@gmail.com and Kenenisa Dekeba; kenenisa.dekeba@wku.edu.et Received 21 January 2022; Revised 30 March 2022; Accepted 15 April 2022; Published 18 May 2022 Academic Editor: Ziya Uddin Copyright © 2022 Gouse Baig Mohammad et al. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. In the past few years, remote monitoring technologies have grown increasingly important in the delivery of healthcare. According to healthcare professionals, a variety of factors influence the public perception of connected healthcare systems in a variety of ways. First and foremost, wearable technology in healthcare must establish better bonds with the individuals who will be using them. e emotional reactions of patients to obtaining remote healthcare services may be of interest to healthcare practitioners if they are given the opportunity to investigate them. In this study, we develop an artificial intelligence-based classification system that aims to detect the emotions from the input data using metaheuristic feature selection and machine learning classification. e proposed model is made to undergo series of steps involving preprocessing, feature selection, and classification. e simulation is conducted to test the efficacy of the model on various features present in a dataset. e results of simulation show that the proposed model is effective enough to classify the emotions from the input dataset than other existing methods. 1. Introduction Affective communication is essential in many fields, in- cluding public health, crisis response, and feedback analysis, to name just a few [1]. Emotions in humans can be expressed vocally, through text or through physical sensations. Humans are capable of recognising a wide range of emotions and thoughts, but computers are unable to distinguish be- tween the intensity and emotion. In the field of research, emotion analysis is a prominent topic since it provides a means of communicating with machines [2]. Despite the fact that researchers have put a lot of effort into text-based emotion recognition, the applications are diverse. In accordance with the literature, textual data, which include social media content and discussion communities, are the primary source of emotion detection [3]. Based on health data, it is also beneficial to distinguish between positive and negative emotions based on health data because Hindawi Computational Intelligence and Neuroscience Volume 2022, Article ID 8787023, 6 pages https://doi.org/10.1155/2022/8787023