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


Affiliations
1 Helwan University, Egypt
2 Business Information System Department, College of Management & Information Technology, Arab Academy for Science, Technology& Maritime Transport, Egypt
3 Department of Information Systems, Helwan University, Egypt
 

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.


Keywords

Business Intelligence, HealthCare, Data Mining, Data-Driven Decision Support System.
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  • Abd Elaziz, L., Sharaf, A., Nasr, M. (2016), Personal Integrated Electronic Health Record, International Journal of Computer Applications (IJCA), Volume 150 – No.12, September 2016, pp 44-47.

  • Ashrafi, N. et al (2014), The impact of Business Intelligence on Healthcare Delivery in the USA, Interdisciplinary Journal of Information Knowledge and Management, 117-130.

  • Anand, R et al (2013), A Data Mining Framework for Building Health Care Management System, International Journal of Engineering Research & Technology, 1639-1648.

  • Bonney W. (2013), Applicability of Business Intelligence in Electronic Health Record, The 2ndInternational Conference on Integrated Information, 257-262.

  • Bhuvaneswari K. (2015), A Comparative Analysis of Clustering Techniques using Genetic Algorithm, International Journal of Computer Science and Mobile Computing, 80-86.

  • Fayyad, U. et al (1996), From Data Mining to Knowledge discovery in Database, American Association of Artificial Intelligence Magazine, 37-54.

  • Garets, D., Davis, M. (2006), Electronic Medical Record vs. Electronic Health Record: Yes, There Is a Difference, A HIMSS Analytics White Paper, 1-14.

  • Helmi, A., Nasr, M., Farhan, M., (2015) The Pivotal Role of Geospatial Information Systems based on Hybrid Cloud Computing for the Health Sector in Egypt, International Journal of Emerging Trends & Technology in Computer Science (IJETTCS), Volume 4, Issue 5(2), Page 99-103, September-October- 2015.

  • Helmi, A., Farhan, M., Nasr, M., (2018) A Framework for Integrating Geospatial Information Systems and Hybrid Cloud Computing, Computers & Electrical Engineering Journal, Elsevier, Volume 67, April 2018, pp. 145-158.

  • Helmi, A., Nasr, M., Mohamed, R. (2017)., Cloud based Spatial Analysis for the Health Sector: A Case Study of Egypt, International Journal of Advanced Networking and Applications (IJANA), Volume 09, Issue 02, Sep - Oct 2017 issue, pp. 3387-3390.

  • Hoyt, R. (2009), Medical Informatics (Practical Guide for Healthcare Professional), Florida: Lulu.com, Electronic Health Record. (n.d.), Retrieved from HIMSS: http://www.himss.org/library/ehr/?navItemNumber=13261

  • Jensen, P., et al (2012), Mining Electronic Health Record: Towards better research applications and clinical care, Nature Reviews, 395-405.

  • Kavitha, S. (2012), Monitoring of Diabetes with Data Mining via CART Method, International Journal of Emerging Technology and Advanced Engineering, 157-162.

  • Khedr, A et al (2016), A proposed Electronic Health Record Content Structure based on Clinical organizational survey, 1-15.

  • Lyer, A., et al (2015), Diagnosis of Diabetes using Classification Mining Technique, International journal of Data Mining & Knowledge Management Process, 1-14.

  • Milovic, B. et al (2012), Prediction and Decision Making in Health Care using Data Mining, International Journal of Public Health Science, 69-78.

  • Rajesh, S. (2012), Application of Data Mining Methods and Techniques for Diabetes Diagnosis, International Journal of Engineering and Innovative Technology, 224-229.

  • Rajkumar, R. (2010), Diagnosis of heart disease using data mining algorithm, Global Journal of Computer Science & Technology, 38-43.

  • Stackowiak, R. a. (2007), Oracle Data warehouse and Business Intelligence Solutions, Wiley Publishing.

  • Soni, Y. et al (2011), Predictive Data Mining for Medical Diagnosis: An overview of heart disease Prediction, International Journal of Computer Application, 43-48.

  • Srinivas, R. (2010), Application of Data Mining techniques in healthcare & Prediction of heart attacks, International Journal on computer science and engineering, 250-255.

  • Sudhakar, K. et al (2014), Study of Heart Disease Prediction Using Data Mining, International Journal of Advanced Research in Computer Science and Software Engineering, 1157-1160.

  • Sultan, T., Nasr, M., Khedr, A., Abdou, R., (2013) A Proposed Integrated Approach for BI and GIS in Health Sector to support Decision Makers (BIGIS-DSS), In the International Journal of Advanced Computer Science and Applications (IJACSA), January 2013, Volume 4, No.1, pp. 170-176.

  • Tvrdikova, M. (2007), Support of Decision Making by Business Intelligence Tools, 6th International Conference (p. 368), Computer information system and industrial management application.

  • Wang, Y. (2010), Business Intelligence and Data Mining in MBS Carbon management.


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

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Authors

Soha Ahmed
Helwan University, Egypt
Ahmed Ibrahim El Seddawy
Business Information System Department, College of Management & Information Technology, Arab Academy for Science, Technology& Maritime Transport, Egypt
Mona Nasr
Department of Information Systems, Helwan University, Egypt

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.


Keywords


Business Intelligence, HealthCare, Data Mining, Data-Driven Decision Support System.

References