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Framework for Data Mining In Healthcare Information System in Developing Countries: A Case of Tanzania

TLDR
This study has proposed a best fit for data mining techniques in healthcare based on a case study and aims to provide self healthcare treatments where diabetic patients can test their blood sugar level by using e-device, which minimizes time to wait for medical treatments, and minimizes the delay in providing medical treatments.
Abstract
Globally the healthcare sector is abundant with data and hence using data mining techniques in this area seems promising. Healthcare sector collects huge amounts of data on a daily basis. Transferring data into secure electronic system of medical health can save lives and reduce the cost of healthcare services as well as early discovery of contagious diseases with advanced collection of medical data. In this study we have proposed a best fit for data mining techniques in healthcare based on a case study. The proposed framework aims to provide self healthcare treatments where by several monitoring equipments using the cyberspace devices have been developed to help patients manage their medical conditions at home for example, diabetic patients can test their blood sugar level by using e-device, which ,with the click of a computer mouse, downloads the results to a healthcare practitioner, minimizes time to wait for medical treatments, and minimizes the delay time in providing medical treatments. Data mining is a new technology used in different types of sectors to improve the effectiveness and efficiency of business model as well as solving problems in business world.

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Book ChapterDOI

A Spending Spree

Gylfi Zoega
TL;DR: In this paper, Iceland's economic turbulence sounds like a familiar macroeconomic story, a credit expansion fuelled excessive borrowing and spending. But there are unfamiliar details -an unusually large banking sector and a central bank unable to serve as a credible lender of last resort -that raise concerns.
Proceedings ArticleDOI

Cloud-based Data Mining Framework: A Model to Improve Maternal Healthcare

TL;DR: The proposed framework model employing cloud computing and data mining is recommended to remarkably improve the administration on the provision of medicines and health supplies, guaranteeing its auspicious accessibility for the benefit of Filipino pregnant women.
Book ChapterDOI

Perspective Approach Towards Business Intelligence Framework in Healthcare

TL;DR: This paper attempts to illustrate the BI approaches incorporated with data mining techniques appropriate in the healthcare domain to overcome the issues and challenges more efficiently.
References
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Journal ArticleDOI

Drug exposure side effects from mining pregnancy data

TL;DR: An interdisciplinary collaborative research project between the Epidemiology Department and the Computer Science Department for using data mining technique to analyze data from pregnant women to derive possible side effects due to exposure to multiple drugs at different duration of the pregnancy.
Journal Article

Lean Software Process

TL;DR: This paper shows how the concepts of lean manufacturing can be successfully applied to software development and confirms that lean software development can produce rapid quality and productivity gains.
Journal Article

Enhancing Enterprise Decisions through Organizational Data Mining

TL;DR: Organizational Data Mining (ODM) is defined as leveraging data mining tools and technologies to enhance the decision-making process by transforming data into valuable and actionable knowledge.
Journal Article

Life cycle of a data warehousing project in healthcare.

TL;DR: The "classical" approach is being taken: enhancing the ODS, which is largely normalized in structure, and integrating data from various sources, along with enforcing business rules.
Journal ArticleDOI

Hierarchical Analysis for Discovering Knowledge in Large Databases

TL;DR: A goal-question-metric paradigm for selecting and implementing metrics for data mining, as well as a case study to illustrate that paradigm, is presented to increase customer satisfaction and retention rate.
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