Multi-Features Disease Analysis Based Smart Diagnosis for COVID-19

Coronavirus 2019 (COVID -19) is the current global buzzword, putting the world at risk. The pandemic's exponential expansion of infected COVID-19 patients has challenged the medical field's resources, which are already few. Even established nations would not be in a perfect position to manage this epidemic correctly, leaving emerging countries and countries that have not yet begun to grow to address the problem. These problems can be solved by using machine learning models in a realistic way, such as by using computer-aided images during medical examinations. These models help predict the effects of the disease outbreak and help detect the effects in the coming days. In this paper, Multi-Features Decease Analysis (MFDA) is used with different ensemble classifiers to diagnose the disease's impact with the help of Computed Tomography (CT) scan images. There are various features associated with chest CT images, which help know the possibility of an individual being affected and how COVID-19 will affect the persons suffering from pneumonia. The current study attempts to increase the precision of the diagnosis model by evaluating various feature sets and choosing the best combination for better results. The model's performance is assessed using Receiver Operating Characteristic (ROC) curve, the Root Mean Square Error (RMSE), and the Confusion Matrix. It is observed from the resultant outcome that the performance of the proposed model has exhibited better efficient. © 2023 CRL Publishing. All rights reserved.

Multi-Features Disease Analysis Based Smart Diagnosis for COVID-19

Coronavirus 2019 (COVID -19) is the current global buzzword, putting the world at risk. The pandemic’s exponential expansion of infected COVID-19 patients has challenged the medical field’s resources, which are already few. Even established nations would not be in a perfect position to manage this epidemic correctly, leaving emerging countries and countries that have not yet begun to grow to address the problem. These problems can be solved by using machine learning models in a realistic way, such as by using computer-aided images during medical examinations. These models help predict the effects of the disease outbreak and help detect the effects in the coming days. In this paper, Multi-Features Decease Analysis (MFDA) is used with different ensemble classifiers to diagnose the disease’s impact with the help of Computed Tomography (CT) scan images. There are various features associated with chest CT images, which help know the possibility of an individual being affected and how COVID-19 will affect the persons suffering from pneumonia. The current study attempts to increase the precision of the diagnosis model by evaluating various feature sets and choosing the best combination for better results. The model’s performance is assessed using Receiver Operating Characteristic (ROC) curve, the Root Mean Square Error (RMSE), and the Confusion Matrix. It is observed from the resultant outcome that the performance of the proposed model has exhibited better efficient. © 2023 CRL Publishing. All rights reserved.

Remote Health Monitoring using IoT and Edge Computing

The Healthcare sector has significantly developed a lot. All Nations started giving more importance to the medical field with the appearance of the new Covid19. But, some countries have been struggling with poor infrastructure in the form of insufficient medical equipment and a shortage of manpower in the medical stream. In such a situation, Remote health monitoring will lighten the burden and ease the process. Currently, smart healthcare is a combination of IoT and cloud architecture. The IoT sensors keep track of patients' health and provide clinically relevant data, which can be used for further processing. If we send all of the raw data generated by the sensors to the cloud for bulk data processing and analysis to make real-time decisions. It would impose several risks such as latency issues, bandwidth congestion, network reliability, high storage cost, and security-related issues which can negatively impact the healthcare industry on whole. To overcome the abovementioned issues, we offer 'Edge Computing, A new emerging technology that allows data processing to be done closer to the data generating device'. Our main aim is to strengthen the existing system. In this paper, the proposed system will continuously collect three main vital parameters from patients in real-time and process the collected data both on the cloud and edge architecture to compare and determine which technology is best suited for time-sensitive applications. © 2022 IEEE.

Immunotherapy during COVID-19 pandemic: An experience at a tertiary care center in India

Introduction: COVID-19 pandemic has been a curse for cancer patients. The lack of understanding and unawareness in handling cancer patients during this pandemic has worsened their conditions. To analyze the real-world scenario, we studied 13 patients who were given immunotherapy during this COVID pandemic era and tried to analyze their outcome or any serious adverse effect that they suffered. This was a pilot study which would pave the way for further bigger studies in future. The aim of the study was to collect the details of patient receiving immunotherapy during COVID-19 pandemic. The data collected included the diagnosis, certain investigations, and the effects of the immunotherapy drugs and its side effects. Results: During this COVID pandemic period starting from March 20 to June 20, we have been regularly giving immunotherapy drugs such as nivolumab, pembrolizumab, and atezolizumab to our patients. We had given six patients nivolumab, six patients pembrolizumab, and one patient atezolizumab. Of the 13 patients who continued to receive immunotherapy in COVID pandemic era, 4 patients were receiving immunotherapy for lung cancer, 3 for head-and-neck malignancy, 2 for relapse lymphoma, and 1 each for hepatocellular carcinoma, renal cell cancer, malignant melanoma, and soft-tissue cancer. One of the patients receiving atezolizumab had actually progressed after receiving pembrolizumab. There was no Grade 3 or 4 toxicity to these drugs and most of our patients continued to be in stable disease/partial remission. One patient had died just after receiving one cycle of nivolumab. Conclusion: COVID-19 infection has posed an unforeseen predicament both for the patients and the treating oncologist. In absence of any previous data, it is very difficult to manage cancer patients where the treatment itself is thought to harm the patients. This is a humble effort to bring to the notice of the world that immunotherapy can be continued during COVID pandemic, provided we take all due precautions.