Abstract: Over the past decade, machine learning has become a game-changing technology in healthcare. In detecting and predicting severity of COVID-19, they build machine learning models that use two datasets: the CT images for COVID-19 patients and those of normal patients and are found to obtain an accuracy of 99.1 percent. Here, researchers propose a cloud approach using two machine learning models that are trained and tested on COVID-19 and non-COVID-19 (normal) patients by using only data of normal CT chest images. Each of their models predicts COVID-19 patients and severity levels. Major findings would help primary healthcare centers, general medical practitioners, and healthcare workers located far away from hospitals or web-based healthcare systems to care for patients at risk of COVID-19. The rapid increase in health-related data opens a new dimension for information and knowledge discovery in the health domain. Machine learning (ML)/deep learning (DL) technology has attracted a lot of attention in healthcare data analytics among researchers from both academia and industry. ML/DL models are being developed to provide early expertise and intelligent decision support for healthcare systems. Traditional ML approaches rely heavily on a great deal of high-quality labeled data. However, circa 80% of healthcare data is typically stored in unstructured forms such as short messages, free-text records, or images. Such a high percentage of free-text data presents both an opportunity and a challenge for data mining in the healthcare domain. Free-text data offers a fantastic chance for healthcare analytics. However, the information contained in these unstructured data is often not utilized for reasons such as data sparsity, lack of clean patterns, or increased noise. Most regions of the world have stepped into the twenty-first century with a legacy health infrastructure largely based on conventional thinking. Health systems in many developing countries, especially, are still struggling to meet the basic needs of their populations. Many MICs face a tsunami of change, driven by urbanization, demographic transition to aging, and cancer and productivity loss due to COVID-19.
Accurate assessment of the health burden from these demographics for timely predictions of societal long-term impact is a major challenge. Despite facing many challenges, some LMICs have made rapid strides in improving the quality and efficiency of their health systems through leapfrogging. Earth observation offers an amazing potential to provide scalable information highly relevant to health inequalities. The integration of inferring and predicting health burden from earth observation and environmental and climate data with insights from artificial intelligence holds great potential in a further leap forward for the understanding of health inequalities and improvement of the equity of health decisions in sustainable urbanization and development. Cloud computing provides the construction of intelligent and scalable multimedia healthcare systems. However, the latest ML/DL approaches still lack reliable and practical public platforms.
Keywords: Cloud Computing,Machine Learning (ML),Real-Time Diagnosis,Predictive Analytics,Healthcare Analytics,Artificial Intelligence (AI),Health Data Streaming,Telemedicine,Medical Data Prediction,Cloud Infrastructure,Big Data in Healthcare,Data Security in Healthcare,Diagnostic Algorithms,Medical IoT (Internet of Things),Health Monitoring Systems
Copyright Form