Blockchain-based EHR System for Indian Healthcare Industry using Aadhar

In today's world, digital technologies are advancing at a rapid pace. Almost every industry has benefited from this ongoing change. In the health sector, the digitization of medical records was proposed decades ago. Whereas some developed countries have successfully adopted and implemented Electronic Health Record (EHR) systems. Developing countries like India still heavily rely on paper-based medical records. Although there are a number of systems for electronic medical record management, they have issues related to interoperability, timely access, and storage. Due to poor infrastructure and design, the current systems are not robust for communicating and tracking medical records. The need for a better EHR system was highly emphasized during the COVID-19 pandemic. The two major shortcomings of the existing system are a lack of interoperability, which causes delays in sharing the information, and a lack of standardization, due to which the data quality of the data that is shared suffers. To mitigate these issues, we need a nationwide EHR system. Another issue is the lack of a ubiquitous UPI (Unique Patient Identifier). In a country like India, the second most populated country in the world, Aadhar is the best option for UPI, which can be used for creating a national EHR system. In this paper, we have presented a framework for a standardized, interoperable, and unified EHR system based on blockchain technology with Aadhar as the UPI. Using blockchain as the base of this model provides numerous advantages over a cloud-based system, like decentralization, better security, immutability, and traceability. © 2023 IEEE.

Covid-19 and Diabetes Risk Prediction for Diabetic Patient using Advance Machine Learning Techniques and Fuzzy Inference System

Diabetes has become a common and critical disease which generally occurs due to the presence of high sugar in blood for long time. A diabetic patient has to follow different rules and restrictions where he/she has to be under proper attention by measuring diabetes level frequently to avoid unexpected risk. The risk become more when patient even doesn't know that he/she is already having diabetes and doesn't follow those restrictions. To prevent this risk, everyone should check the diabetes status to be sure. With the same target different system using machine learning techniques have been introduced which can predict the diabetes status of a patient. But the challenging fact is that the performances and accuracy of those models are questionable where there may be a huge risk of patient's life. The conventional systems are not able to show that which level of diabetes a patient can have using the previous records. To solve this issue, through this paper an efficient system has been proposed with which the diabetes status can be predicted correctly. The proposed system can also show the complexity of diabetes as well as the Covid-19 risk percentage that can also be possible to measure. After comparing several machine learning techniques, the suitable model has been selected where high level of accuracy has been ensured in term of predicting the disease. © 2022 IEEE.

Real-Time Face Mask Detection Using Streamlit,TensorFlow, Keras and Open-CV

This paper is based on the protection of our health from coronavirus officially known as COVID-19. Real-time detection of a face mask can help to prevent of the coronavirus, detecting the mask with the help of machine learning and data science algorithms such as Streamlit, MoblieNetV2, OpenCV, etc., are widely used in this ideal methodology. This paper is about the method that provides an accuracy of 99.78% in detecting the mask with live video stream. The method proposes building accurate model and integrating the model with a graphical interface which can improve the experience of the user. © 2022 IEEE.

Machine Learning - Based Diagnosis of Covid-19 using Clinical Data

Coronavirus (COVID-19) is a worldwide pandemic caused by SARS Coronavirus 2. (SARS-CoV-2). The COVID-19 epidemic has put global healthcare systems in jeopardy. This study's purpose is to develop and evaluate an automated COVID-19 infection detection system using machine learning and chest x-ray images. Early diagnosis and treatment may help avert major illness and even death. It is presently the most favoured and accurate approach for COVID-19 diagnosis. X-ray imaging of the chest may be used instead of the rRT-PCR test to look for early COVID-19 symptoms. A new machine learning (ML)-based analytical framework for automated COVID-19 diagnosis is created utilizing chest X-ray pictures of likely patients. The proposed framework for COVID-19 disease diagnosis using X-ray images has a 99 percent accuracy for Covid and a 92 percent accuracy for Non-covid in two-class categorization. The investigation suggests the COVID-19 detection framework is better. © 2022 IEEE.

A Web-based Blood Bank System for Managing Records of Donors and Receipts

The Online Blood Donation Management System, the purpose of which is to act as a bridge between a person who needs blood, a patient, and a blood donor. The design of an automatic blood system has become an integral part for saving the human lives, who need the blood under different situations. Since, there are various drawbacks of the pre-existing system like privacy issues for the donors, which are getting reflected directly on the interface. Thus, we have designed a robust system that will create a connection between different hospitals, NGOs, and blood banks to help the patient in any difficult situation. Thus, HIPPA model provides a backbone for security breaches The interface designed will be easy-to-use and easy to access and will be a fast, efficient, and reliable way to get lifesaving blood, totally free of charge. Apart from this the visualization of the data is present along with the one extra COVID module, which will help covid and normal patients for plasma donation. The main aim of the paper is to reduce the complications of finding a blood donor during panic situations and provide a high level of security for the donors. © 2022 IEEE.