Big Data-Based Optimized Deep Learning Model for Improving Performance of Electronics Health Care Data

Bigdata analytics is a new area of supervised analytics that healthcare analytics has moved into. Healthcare data is transmissive, incremental, and substantial, and it has pre-set thresholds for classifying patient conditions and diseases. The necessary understanding of disease occurrences is applied to the analysis of this data. The research project introduces healthcare data analytics with a novel framework that leverages unstructured data for the classification of healthy and unhealthy samples in order to understand the nature of healthy and unhealthy labelled classes. This is motivated by the fact that healthcare data is informative and voluminous in nature. Additionally, the unknown samples\u27 health status is predicted using the supervised knowledge. Each model presented in this work is supported by accuracy and a computational complexity model

Data-Driven Public Health Surveillance: Identifying Influenza Trends via Social

The internet era makes it appear outdated to monitor and identify influenza using traditional methods. Among the long-term public health problems that influenza might exacerbate include diabetes, asthma, congestive heart failure, sinus infections, ear infections, and bacterial pneumonia. Deep learning (DL) techniques for influenza identification are more efficient than traditional approaches in terms of logistics and cost. The benefit of influenza prediction lies in its ability to minimize morbidity and mortality by allowing relevant departments to implement appropriate preventative and control actions after evaluating forecasted data. This research develops a Runge Kutta optimized Dynamic Gated recurrent unit (RKO-DGRU) public health with for influenza identification. Initially, the dataset is collected from kaggle and preprocessed utilizing the lemmatization method. Our approach can result in a sensitivity of 86.69%, specificity of 93.68%, and 97.5% accuracy. The findings highlight the possibility of applying DL approaches to efficiently identify and categorize influenza using data gleaned from conversations on open networks. It can thus provide efficient ways to stop and manage an Influenza epidemic

Evaluating the Impact of Universal Health Coverage Policies on Population Health Outcomes Using Big Data Analytics for Comprehensive Policy Assessment

Universal Health Coverage (UHC) aims to eliminate financial barriers to care which is important for everyone’s affordability. Thus, it is helpful to study how UHC affects population health outcomes and where it may be headed in the future. Big Data Analytics provides good tools for scrutinizing those principles and their results on a huge scale. Communities differ in terms of their health outcomes; and health systems are complex. This paper tries to address these among other issues by integrating big data sets that are diverse and analysing them together. As an innovative way of examining universal health coverage initiatives’ impact on populations, researchers propose the Comprehensive Analysis of Fragmented Health Systems (CA-FHS). CA-FHS uses Big Data Analytics to compile and analyse information from many different sources thereby giving an exhaustive breakdown of health outcomes by demography as well as geographic area. This would allow trends or patterns not seen using conventional evaluation tools to be discovered. It encompasses public health, policy-making, as well as healthcare management concerns. In this way, the method may bring out the strengths and weaknesses inherent in the healthcare system such that policies are recommended for change while resource allocation is done for bettering UHC-related consequences internationally. It will enable the evaluation of long-term effects resulting from these projects so that they can meet their goals eventually. The process will involve creating hypothetical policy scenarios and then assessing how these would affect population’s historical health outcomes through use of historical data simulation techniques; thus providing input into potential policy choices at federal level concerning evidence-based recommendations towards achieving improvement in health status

Design of Epidemic Model For Covid-19 Disease Prediction Using Deep Learning

Promising technologies are available in the developing field of Public Health Surveillance (PHS) to help public health authorities make decisions more quickly by expediting the process of monitoring, analysing, and using unofficial sources. The cornerstone of public health practice is public health surveillance. Influencing policy decisions, spearheading new program initiatives, improving public relations, and helping organisations assess their research expenditures all depend on surveillance data. Public health experts may find that mathematical models are an effective tool in controlling epidemics, which might result in a significant drop in the number of cases and deaths. Moreover, decision-makers can optimise prospective control strategies, including as vaccination campaigns, lockdowns, and containment measures, by using mathematical models to produce long- and short-term forecasts. This work suggests the evolution of epidemics