Using Data Science Software to Address Health Disparities

The article assesses data science software to evaluate the usefulness of data science technology in addressing concerns such as health disparities. Data science software was analyzed using KDnuggets data related to analytics, data science, and machine learning software. Data science functionalities include computational processes and frameworks that are relevant for healthcare. This study demonstrates the importance of leading applications for conducting data science operations that can improve care in healthcare networks by addressing such factors as health disparities.

Big Data Applications in Vaccinology

The development of vaccines has been one of the most important medical and pharmacological breakthroughs in the history of the world. Besides saving untold lives, they have enabled the human race to live and thrive in conditions thought far too dangerous only a few centuries ago. In recent times, the development of the COVID-19 vaccine has captured the world’s attention as the primary tool to defeat the current pandemic. The tools used to develop these vaccines have changed dramatically over time, with the use of big data technologies becoming standard in many instances. This study performs a structured literature review centered on the development, distribution, and evaluation of vaccines and the role played by big data tools such as data analytics, datamining, and machine learning. Through this review, the paper identifies where these technologies have made important contributions and in what areas further research is likely to be useful.

Ontology-based IoT healthcare systems (IHS) for Senior Citizens

Rapid incremental growth in population causes the virulence of infectious diseases worldwide. Due to this, health hazards with population growth raise pollution in the air, water, and soil and affect the immunity of individuals. To handle the situation, reliable and easy to reach healthcare services are required. The proliferation of connected technologies along with the Internet of Things (IoT) is providing modern healthcare with extensive care. All-pervading IoT technology gaining a very much attraction nowadays. This paper presents a brief about the E-Health Care System along with its framework. This attempt also presents the ontology approach as data produced by healthcare applications is vast and unstructured which needs to be organized in proper format with a smooth flow of data and also results in less request-response time. Further, this paper discusses the impact of the disease on senior citizens in the current scenario.

Comprehensive Analysis of State-of-the-Art CAD Tools and Technique for Chronic Kidney Disease (CKD)

It has been seen that in the last one decade, AI/ML/DL has been considered a core research area in healthcare as we know that kidney is one among the important internal body organs helps in regulation of the fluid within the body such that it relieves the body from the existence of the waste in the body. it is difficult to detect early on by normal clinical process. Many researchers have focused their work to identify the kidney disease or classify the kidney disease using computational technology because of the mortality rate is very high in kidney patients. Primary focus of this paper is review the current research work based on computational advancement in the area of kidney disease and also identify the gaps or future scope to identify and predict the kidney disease at earlier stage.

Different Approaches to Reducing Bias in Classification of Medical Data by Ensemble Learning Methods

In this study, different models were created to reduce bias by ensemble learning methods. Reducing the bias error will improve the classification performance. In order to increase the classification performance, the most appropriate ensemble learning method and ideal sample size were investigated. Bias values and learning performances of different ensemble learning methods were compared. AdaBoost ensemble learning method provided the lowest bias value with n: 250 sample size while Stacking ensemble learning method provided the lowest bias value with n: 500, n: 750, n: 1000, n: 2000, n: 4000, n: 6000, n: 8000, n: 10000, and n: 20000 sample sizes. When the learning performances were compared, AdaBoost ensemble learning method and RBF classifier achieved the best performance with n: 250 sample size (ACC = 0.956, AUC: 0.987). The AdaBoost ensemble learning method and REPTree classifier achieved the best performance with n: 20000 sample size (ACC = 0.990, AUC = 0.999). In conclusion, for reduction of bias, methods based on stacking displayed a higher performance compared to other methods.

Improvement in Task Scheduling Capabilities for SaaS Cloud Deployments Using Intelligent Schedulers

Task scheduling on the cloud involves processing a large set of variables from both the task side and the scheduling machine side. This processing often results in a computational model that produces efficient task to machine maps. The efficiency of such models is decided based on various parameters like computational complexity, mean waiting time for the task, effectiveness to utilize the machines, etc. In this paper, a novel Q-Dynamic and Integrated Resource Scheduling (DAIRS-Q) algorithm is proposed which combines the effectiveness of DAIRS with Q-Learning in order to reduce the task waiting time, and improve the machine utilization efficiency. The DAIRS algorithm produces an initial task to machine mapping, which is optimized with the help of a reward & penalty model using Q-Learning, and a final task-machine map is obtained. The performance of the proposed algorithm showcases a 15% reduction in task waiting time, and a 20% improvement in machine utilization when compared to DAIRS and other standard task scheduling algorithms.

ICTs and Domestic Violence (DV)

The use of information communication technologies (ICTs) to empower individuals through social support, help-seeking, and help-providing activities is finding its place in healthcare delivery. ICTs, in particular, offer access to timely and relevant information that domestic violence victims and organizations can tap into. Thus, this article explores the use of ICTs for providing and facilitating support and care-giving services to victims/survivors of domestic violence with online communities and other groups.

A Predictive Analytics Framework for Blood Donor Classification

India faces numerous challenges to the meet ever-increasing demand of human blood so as to improve the health indicators across its rural and urban population. The gap between demand and supply can be fulfilled by increasing voluntary blood donations. Hence, it becomes important to understand the attitude of population towards blood donations. In this paper an effort has been made to identify features in order of their importance that affect the decision of a person to become a blood donor. This research uses extensive visualization techniques to get an insight into potential blood donor characteristics and then applies classification technique to classify youth of an Indian state university as donor or non-donor. The k-nearest neighbour classification algorithm discovers the relationship between attributes of blood donors and hence predicts the outcome. The important factors that dissuade potential donors from donating blood have been extracted that can be worked upon to meet the demand of blood to save human lives.

Predicting Inpatient Status for the Next 30/60/90 Days With Machine Learning

In this paper, we report the development of machine learning techniques which can help hospital authorities assess a patients' medical condition and also calculate the probability of readmission of the patient as inpatient, and thus identify patients with higher risks for readmissions. Factor Analysis is performed on patient data to understand the severity of mental health, and Random Forest models are used to determine the probability of a patient becoming an inpatient for the next 30/60/90 days from their last visit to the physician’s office. The Random Forest model fits the data with an overall OOB Error rate of 3.69% and an accuracy of 97.65%. The accuracy on the test data was 96.11%. A web application is also developed to provide a user-friendly interface for physicians and administrators to interact with and obtain relevant information for a given patient and or a group of patients. The web application affords physicians additional inputs to assist in their diagnosis and administrators, a window into anticipating and preparing for future patient needs.

Predicting Inpatient Status for the next 30/60/90 Days with Machine Learning

In this paper, we report the development of machine learning techniques which can help hospital authorities assess a patients' medical condition and also calculate the probability of readmission of the patient as inpatient, and thus identify patients with higher risks for readmissions. Factor Analysis is performed on patient data to understand the severity of mental health, and Random Forest models are used to determine the probability of a patient becoming an inpatient for the next 30/60/90 days from their last visit to the physician’s office. The Random Forest model fits the data with an overall OOB Error rate of 3.69% and an accuracy of 97.65%. The accuracy on the test data was 96.11%. A web application is also developed to provide a user-friendly interface for physicians and administrators to interact with and obtain relevant information for a given patient and or a group of patients. The web application affords physicians additional inputs to assist in their diagnosis and administrators, a window into anticipating and preparing for future patient needs.