Journal Article

Sustainable environment in disaster management-based healthcare system using artificial intelligence

Suhasini, S., Ramudu, Kama, Prasad, Somarouthu V. G. V. A., Rajpoot, Pushp Lata, Jain, Aaditya, Kondaveeti, Suresh Babu

2026

Overview

The application of machine learning (ML) methods and predictive analytics in disaster management has made a drastic change in this field over the past few years. With their unparalleled ability to forecast, prepare, and respond, these advanced technologies are transforming the complete paradigm of disaster and emergency management. Much of this work is reinforced by machine learning models, an artificial intelligence domain that analyses huge amounts of data to establish patterns and forecast future disasters. This research proposes novel techniques in disaster management-based healthcare system utilizing machine learning model for sustainable environment. The study utilizes a dataset Centre for Research on Epidemiology of Disasters (CRED) launched Emergency Events Database (EM-DAT) in 1988. Data on frequency as well as effects of about 15,700 incidents since 1900 can be found in International Disaster Database, or EM-DA, which is preprocessed for noise removal and normalization. The processed data features have been extracted utilizing deep adversarial gaussian multilayer perceptron and the features has been optimized using firefly swarm binary grasshopper optimization. Experimental analysis is carried out in terms of random accuracy, precision, recall, AUC, F-1 score. Proposed technique random accuracy 98%, precision 95%, F-1 score 94%, AUC 96%, Recall 97%.

• Publisher: BioMed Central,Nature Publishing Group,Springer
• Subject: Accuracy / Artificial intelligence / Binary grasshopper optimization / Data base management systems / Datasets / Decision making / Disaster management / Disasters / Earthquakes / Emergency management / Emergency preparedness / Epidemiology / Floods / Health care / Health Sciences / Healthcare system / Learning algorithms / Machine learning / Machine learning model / Medicine / Medicine & Public Health / Multilayer perceptrons / Neural networks / Predictive analytics / Public Health / Recall / Research Methodology / Sensors / Statistics for Life Sciences / Sustainable environment