Performance Analysis of Different Classification Methods in Data Mining for Diabetes Dataset Using WEKA Tool

Data mining is the process of analyzing data based on different perspectives and summarizing it into useful information. Classification is one of the generally used techniques in medical data mining. The goal here is to discover new patterns to provide meaningful and useful information for the users. Recently data mining techniques are applied to healthcare datasets to explore suitable methods and techniques and to extract useful patterns. This paper includes implementation of different classification methods, measures, analysis and comparison pertaining to diabetes dataset. A detailed performance analysis and comparative study of these methods are done, which can be further used to choose the appropriate algorithm for future analysis for the given dataset. DOI: 10.17762/ijritcc2321-8169.15036

Prioritizing Interventions and Research to Address the Cancer Disparities of Arizona’s American Indian Population

The aim of the Southwest American Indian Collaborative Network (SAICN) is to reduce cancer disparities by closing the gap between community needs and the promise of cancer prevention and cure through participatory education, training and research programs. In an effort to provide evidence-based recommendations and promote the use of relevant data in tribal communities, the SAICN Data and Evaluation Core developed two comparison matrices that present scientifically sound practices for use by community health decision makers in prioritizing activities likely to reduce the irrespective community’s burden of cancer. In their current configurations, Matrix A considers those cancers for which prevention and early detection interventions exist (cervical, breast, colorectal, tobacco-linked) and Matrix B addresses cancers for which interventions are unknown or not well developed. The matrices were converted into worksheet formats to facilitate their use at the community level. Further, to facilitate the application of this approach in a tribal community setting, guidelines for a five-part implementation plan were developed. In this paper, we describe the matrices and the guidelines and our process for moving forward

Online Brand Communities and their Impact on Brand Equity of Indian Telecommunication Industry

Telecommunication space in India has become highly competitive and hence organizations are looking for newer value propositions and innovative ways to compete. With the advent of digital media, physical spaces are now being complimented with virtual spaces by organizations as a means of competitive advantage. Online brand communities (OBC’s) is one such source by which Telecom companies can achieve value creation and enhanced online customer engagement with customers.Hence this study is primarily an attempt to examine the impact of Online Brand Communities on selected brand equity dimensions of loyalty, awareness, association and perceived brand quality of telecom service providers. The purpose of this paper is to come up with a conceptual model which can explain the effects of the Online Brand Communities on value of the brand (brand equity) of telecommunication service providers.Primary data was collected from a sample of 120 respondents with the help of a questionnaire. For data analysis, statistical methods like factor analysis and regression analysis have been used to group inter related variables and predict the relationship between correlated variables respectively.Very few studies on OBC’s have been conducted in the telecommunication space hence the study will add to the academic literature and help telecom managers to determine how brand generated content and community participation can drive business to next level through engaged online customer experience. It will help the telecom companies in analysing brand equity building through online space, in turn enhancing the purchase decision, customer engagement and create competitive advantage

An Empirical Study to Measure Customer Experience for Telecom Operators in Indian Telecom Industry

as Indian Telecom industry matures itself, service providers understand the essence of Customer Experience as the prime differentiator towards business success. This paper will provide a rundown of the extant literature on customer experience studies done in Telecom industry. This research paper also attempts to identify the determinants of Customer Experience for Telecom operators in Indian Telecom industry. Also this research paper defines a yardstick called ACEI score to quantify customer experience in telecom industr

Anomaly Detection in IoT: Recent Advances, AI and ML Perspectives and Applications

IoT comprises sensors and other small devices interconnected locally and via the Internet. Typical IoT devices collect data from the environment through sensors, analyze it and act back on the physical world through actuators. We can find them integrated into home appliances, Healthcare, Control systems, and wearables. This chapter presents a variety of applications where IoT devices are used for anomaly detection and correction. We review recent advancements in Machine/Deep Learning Models and Techniques for Anomaly Detection in IoT networks. We describe significant in-depth applications in various domains, Anomaly Detection for IoT Time-Series Data, Cybersecurity, Healthcare, Smart city, and more. The number of connected devices is increasing daily; by 2025, there will be approximately 85 billion IoT devices, spreading everywhere in Manufacturing (40%), Medical (30%), Retail, and Security (20%). This significant shift toward the Internet of Things (IoT) has created opportunities for future IoT applications. The chapter examines the security issues of IoT standards, protocols, and practical operations and identifies the hazards associated with the existing IoT model. It analyzes new security protocols and solutions to moderate these challenges. This chapter’s outcome can benefit the research community by encapsulating the Information related to IoT and proposing innovative solutions

COVID-19 Severity Among American Indians and Alaska Natives in 16 States - January 1, 2020, to March 31, 2021

Objective: To compare rates and risk factors of severe COVID-19-related outcomes between American Indian/Alaska Native (AI/AN) and non-Hispanic White people (NHW). Methods: Aggregate Social Vulnerability Index (SVI), COVID-19-related risk factor, hospitalization, and mortality data were obtained from 16 states for January 1, 2020-March 31, 2021. Generalized estimating equation Poisson regression models calculated age-adjusted cumulative incidences, incidence ratios (IR), and 95% confidence intervals (CI) comparing AI/AN and NHW persons by age, sex, and county-level SVI status. Results: Race data were missing for 42.7% of COVID-19 cases, 24.7% of hospitalizations, and 10.1% of deaths. Risk of AI/AN COVID-19 mortality was 2.6 times that of NHW persons (IR 2.6, 95% CI: 1.7 – 3.4); risk of COVID-19-related hospitalization among AI/AN persons was 3.5 times that of NHW (IR: 3.5, 95% CI: 2.7 – 4.3). Severe COVID-19 outcomes were significantly higher for AI/AN persons compared to NHW persons across all age and sex groups. There was no statistically significant difference in COVID-19 outcomes by SVI status. Associations between severe COVID-19 outcomes and co-morbid risk factors were inconsistent. Conclusions: Results describe increased risk of severe COVID-19 outcomes for AI/AN persons compared to NHW persons despite quality issues in public health surveillance data. Data linkages and improved ascertainment reduce race/ethnicity misclassification and improve data quality. COVID-19-related health burdens among AI/AN persons warrant improved access for AI/AN communities to medical countermeasures and healthcare resources

Participatory policy analysis in health policy and systems research: reflections from a study in Nepal

Background Participatory policy analysis (PPA) as a method in health policy and system research remains underexplored. Using our experiences of conducting PPA workshops in Nepal to explore the impact of the country’s move to federalism on its health system, we reflect on the method’s strengths and challenges. We provide an account of the study context, the design and implementation of the workshops, and our reflections on the approach’s strengths and challenges. Findings on the impact of federalism on the health system are beyond the scope of this manuscript. Main body We conducted PPA workshops with a wide range of health system stakeholders (political, administrative and service-level workforce) at the local and provincial levels in Nepal. The workshops consisted of three activities: river of life, brainstorming and prioritization, and problem-tree analysis. Our experiences show that PPA workshops can be a valuable approach to explore health policy and system issues – especially in a context of widespread systemic change which impacts all stakeholders within the health system. Effective engagement of stakeholders and activities that encourage both individual- and system-level reflections and discussions not only help in generating rich qualitative data, but can also address gaps in participants’ understanding of practical, technical and political aspects of the health system, aid policy dissemination of research findings, and assist in identifying short- and long-term practice and policy issues that need to be addressed for better health system performance and outcomes. Conducting PPA workshops is, however, challenging for a number of reasons, including the influence of gatekeepers and power dynamics between stakeholders/participants. The role and skills of researchers/facilitators in navigating such challenges are vital for success. Although the long-term impact of such workshops needs further research, our study shows the usefulness of PPA workshops for researchers, for participants and for the wider health system. Conclusions PPA workshops can effectively generate and synthesize health policy and system evidence through collaborative engagement of health system stakeholders with varied roles. When designed with careful consideration for context and stakeholders’ needs, it has great potential as a method in health policy and systems research

Comparison of recruitment and retention among demographic subgroups in a large diverse population study of diet

Objective We examined the feasibility of conducting a longitudinal study of diet among diverse populations by comparing rates of response throughout recruitment and retention phases by demographic and other characteristics. Methods Using quota sampling, participants were recruited from 3 geographically and demographically diverse integrated health systems in the United States. Overall, 12,860 adults, ages 20–70, were invited to participate via mail. Participation first required accessing the study's website and later meeting eligibility criteria via telephone interview. Enrollees were asked to provide two 24-h dietary recalls, either interviewer-administered or self-administered on the web, over 6 weeks. Stepped monetary incentives were provided. Results Rates for accessing the study website ranged from 6% to 23% (9% overall) across sites. Site differences may reflect differences in recruitment strategy or target samples. Of those accessing the website, enrollment was high (≥87%). Of the 1185 enrollees, 42% were non-Hispanic white, 34% were non-Hispanic black, and 24% were Hispanic. Men and minorities had lower enrollment rates than women and non-Hispanic whites, partially due to less successful telephone contact for eligibility screening. Once enrolled, 90% provided 1 recall and 80% provided both. Women had higher retention rates than men, as did older compared to younger participants. Retention rates were similar across race/ethnicity groups. Conclusions While study recruitment remains challenging, once recruited most participants, regardless of race/ethnicity, completed two 24-h dietary recalls, both interviewer-administered and self-administered on the web. This study demonstrates the feasibility of collecting multiple 24-h recalls including less expensive automated self-administered recalls among diverse populations.Cancer Research Networ

Effects of antiplatelet therapy on stroke risk by brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases: subgroup analyses of the RESTART randomised, open-label trial

Background Findings from the RESTART trial suggest that starting antiplatelet therapy might reduce the risk of recurrent symptomatic intracerebral haemorrhage compared with avoiding antiplatelet therapy. Brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases (such as cerebral microbleeds) are associated with greater risks of recurrent intracerebral haemorrhage. We did subgroup analyses of the RESTART trial to explore whether these brain imaging features modify the effects of antiplatelet therapy