Investigation on improving the performance of class-imbalanced medical health datasets

Data has increased significantly in recent years due to technical and technological advancements, especially in the medical field. Machine learning is an astounding field with precise outcomes in medical domains such as detection, diagnosis, imaging, personalized medicine, etc. The machine learning algorithms analyze the feature-engineered data and produce precise outcomes using different learning methods, such as supervised and unsupervised. In the case of medical applications, these algorithms play a vital role in disease diagnosis and recognition of patterns, even in the absence of medical experts. For instance, during the coronavirus (Covid-19) pandemic, machine learning algorithms have accurately identified the infected persons using chest X-ray recordings, real-time polymerase chain reaction (RT-PCR) tests, and blood samples in the early stages. However, the learning algorithms have certain limitations in recognizing an imbalanced dataset collected for the deadliest diseases such as coronary heart disease, strokes, respiratory illness, COPD, cancers, diabetes, Alzheimer's disease, TB, cirrhosis, etc. To investigate the performance of such a class-imbalanced dataset and to improve its performance in terms of false positive rates, this chapter utilizes various methods such as random oversampling, random undersampling, and SMOTE to handle the class-imbalance problem in two different medical datasets. Then, the balanced dataset is evaluated using algorithms like naive Bayes, decision tree, and k-nearest neighbor in different evaluation models. The learning algorithms are evaluated with familiar metrics-precision, accuracy, recall, F-measure, and ROC area. Experiments on two utilized class-imbalanced datasets show that SMOTE performs better in handling class-imbalance problems. © 2023 River Publishers.

Consumer Perception of Internet Banking and Mobile Banking Using Twitter Analytics

Mobile and internet banking have introduced a new way of monetary transactions without the need for physical presence. This research proposes to analyze the sentiments of people regarding digital transactions, mobile, and internet banking. The explosion of internet usage and the huge funding initiatives in electronic banking have drawn the attention of researchers towards internet and mobile banking. This study focuses on customer value perceptions of the internet and mobile banking in India. The recent and forecasted Digital India scheme shows high growth in e-banking in India. The demographic, attitudinal, and behavioral characteristics of mobile bank users were examined. In this study, datasets obtained from Twitter were used. After extensive and repeated analysis, it is found that both mobile and internet banking are well received;the number of positive tweets, especially regarding mobile banking, is much higher than that of internet banking. This leads to the interpretation that people find mobile banking easier and safer, especially during the ongoing COVID-19 pandemic. © 2022 Information Resources Management Association. All rights reserved.

Perception About Online Classes Specific to Discipline and Living Locality of Students: A Cross-Sectional Study

Online education has gained immense popularity among the working people and students pursuing higher education. Various renowned universities all over the world are offering online degrees and diplomas to all people through digital technologies. This enhances the concept of online classes due to the complete shutdown of educational institutions for an indefinite COVID-19 pandemic situation. Though the online classes are an immediate and emergency paradigm shifting in teaching and learning, it has certain drawbacks that concern the student to a larger extent. To examine the effects of online classes in terms of quality, comfort, and compatibility, this study analyzes the students' perception of various arts, science, and engineering colleges. Drawing on data from various students and its statistical test has inferred various challenges faced by the students with respect to their discipline and living locality. The result of statistical analysis recommends more improvisations and special considerations to the educational institutions to make this mode a viable solution. © 2022 IGI Global. All rights reserved.