A Proposed Framework for Detecting and Predicting Diseases through Business Intelligence Applications

Abstract

The Demand for healthcare IT and its analytics increases in the last few years. To improve quality of care (e.g., ensuring that patients receive the correct medication) which will help to improve the efficiency of clinical quality and safety, operations.The Nature of the medical field is rich with information where there's a variety and abundance of data but untapped in a correct and effective manner to get the right knowledge. and therefore, the most serious challenge facing this area is the quality of service provided which means to make the diagnose in a proper manner at a timely manner and provide appropriate medications to patients because Poor diagnosing can lead to serious consequences which are unacceptable. And because there is a lack of effective analysis tools to discover hidden relationships and trends in data, so Health information technology has emerged as a new technology in health care sector in a short period by utilizing Business Intelligence 'BI' which is a data-driven Decision Support System. Which Was developed from 1990s to now, and gradually become one of the most important information systems applied in any sector. BI enables to deal with huge amount of data and extract useful knowledge to support decision making. Data mining 'DM' is a kind of data processing technology which can be regarded as a part of the BI system, but it can be also considered as an independent and integrated technology which can treat mass data and extract hidden relationships from it.This introduction highlights the main importance of how to apply the business intelligence applications using data mining techniques to help medical professionals in healthcare sector rapidly diagnosing and predicting diseases of any patients not only this but also detecting the disease complications on the patient which will decrease the overall cost of expenditure that the country paid, briefly this is the central research idea which address the motivation for doing this research.

Designing a Clinical Education Tracking System: An Innovative Approach

“…Research has shown BI to be effective in other nonbusiness-related applications, such as sleep medicine, internal medicine, and radiology, etc. [4][5][6][7][8][9][10] As part of BI, the concept of Key Performance Indicators (KPI) are one of the significant tools that can be used to understand customers (stakeholders, such as students, preceptors, clinical sites, accreditation agencies, etc.). 11,12 We believe that clinical education is an obvious application of this technology where data mining and predicting student's success can be developed and achieved as well as using KPIs to understand its data.…”

“…BI involves data mining for trends, data visualization, machine learning, and artificial intelligence to create prediction models. 4 An educator requires a repository of data and mechanisms to continuously collect and analyze data. The ease and availability of an educator to build their own database and use BI as a powerful visualizing tool is possible.…”

Data Mining Algorithm for Cloud Network Information Based on Artificial Intelligence Decision Mechanism

“…The associate editor coordinating the review of this manuscript and approving it for publication was Ying Li. be difficult problems that data analysis technology needs to solve [1]- [3]. This requires analytical technology to filter out low-value or low-density data, and then mine knowledge gold in high-value or high-density data [4], [5]. In recent years, the prosperity of the information industry has spawned a number of new concepts, technologies, and applications such as the Internet, massive data, massive storage, and analysis, all of which have contributed to the prosperity of big data.…”

Diabetes Disease Detection through Data Mining Techniques

“…The predictive power of data mining is generated from principles of pattern recognition, machine learning, and statistics in which they enables it automatically to extract knowledge and also to determine interest interrelations and patterns from large databases. [6] Data mining involves a number of complex and advanced data analysis tools to discover the previously hidden and unknown valid patterns and relationships in large available data sets. These analysis tools can be categorized to mathematical algorithms, statistical methods, and learning algorithms.…”