The paper presents the main research results in the area of data mining application to medicine. We propose a new information technology of data mining for different classes of biomedical images based on the methodology of diagnostically relevant information selection and creation of informative characteristics. Application of Big Data technology in proposed systems of medical diagnostics has allowed to improve the learning set quality and reduce the classification error. Based on these results, the conclusion is made, that the usage of many heterogeneous sources of diagnostic information made it possible to improve the overall quality of the diagnostics.

Particular Use of BIG DATA in Medical Diagnostic Tasks

Large and complex amounts of data are being generated where traditional data processing applications are inadequate to deal with them. On the other hand, there is a growing need for extracting information from operational data in real time, which is crucial in fast changing situation. The faster one can harness insights from data, the greater the benefit in driving value, taking, reducing costs, and increasing efficiency. This paper surveys different approaches in real-time analytics of Big Data or near real-time in the specific fields of application, as well as tools and techniques being used. The survey results indicate what technologies have been used in each of the fields of application and what the reason for the choice was.

A Survey on Real-Time Big Data Analytics: Applications and Tools

Empowerment of big data and eco-system in diabetes retinopathy [retinal images] - a review

in the time of the Information Technology, the big data store is going on. Due to which, Huge amounts of data are available for decision makers, and this has resulted in the progress of information technology and its wide growth in many areas of business, engineering, medical, and scientific studies. Big data means that the size which is bigger in size, but there are several types, which are not easy to handle, technology is required to handle it. Due to continuous increase in the data in this way, it is important to study and manage these datasets by adjusting the requirements so that the necessary information can be obtained. The aim of this paper is to analyze some of the analytic methods and tools. Which can be applied to large data. In addition, the application of Big Data has been analyzed, using the Decision Maker working on big data and using enlightened information for different applications.

Analytical Study on Big Data

Bigdata Analytics on Diabetic Retinopathy Study (DRS) on Real-time Data Set Identifying Survival Time and Length of Stay☆

The Exponential and Weibull distributions are well-known failure time distributions in reliability theory and survival analysis. Order statistics occur naturally in life testing and in survival analysis. The properties of order statistics and the results of order statistics are used to estimate the three-parameter Weibull distributions. This study ranges from order statistics to distribution theory and then to survival analysis. To know the survival distribution function S(t), many distributional forms have been used. Moments of order statistics help us to estimate the one-parameter and two-parameter forms, but to estimate the three-parameter Weibull distribution is a challenging one. Hence we make this study as a twofold study: firstly, to study the order statistics for Weibull distributions and their moments, and secondly, apply the computed moments of order statistics to estimate the location and scale parameters (with the shape parameter being fixed) based on complete as well as type II right-censored samples. To achieve the goal, data have been simulated for the failure time and their moments estimated based on the order statistics, and we explained how the conventional estimators play their role in reliability in order to verify the accuracy of the numerical computations.

V. Sujatha

Papers

Bigdata Analytics on Diabetic Retinopathy Study (DRS) on Real-time Data Set Identifying Survival Time and Length of Stay☆

Weibull Estimates in Reliability: An Order Statistics Approach