CFP last date
22 April 2024
Reseach Article

Automated Disease Prediction System (ADPS): A User Input-based Reliable Architecture for Disease Prediction

by Md. Tahmid Rahman Laskar, Md. Tahmid Hossain, Abu Raihan Mostofa Kamal, Nafiul Rashid
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 133 - Number 15
Year of Publication: 2016
Authors: Md. Tahmid Rahman Laskar, Md. Tahmid Hossain, Abu Raihan Mostofa Kamal, Nafiul Rashid
10.5120/ijca2016908193

Md. Tahmid Rahman Laskar, Md. Tahmid Hossain, Abu Raihan Mostofa Kamal, Nafiul Rashid . Automated Disease Prediction System (ADPS): A User Input-based Reliable Architecture for Disease Prediction. International Journal of Computer Applications. 133, 15 ( January 2016), 24-29. DOI=10.5120/ijca2016908193

@article{ 10.5120/ijca2016908193,
author = { Md. Tahmid Rahman Laskar, Md. Tahmid Hossain, Abu Raihan Mostofa Kamal, Nafiul Rashid },
title = { Automated Disease Prediction System (ADPS): A User Input-based Reliable Architecture for Disease Prediction },
journal = { International Journal of Computer Applications },
issue_date = { January 2016 },
volume = { 133 },
number = { 15 },
month = { January },
year = { 2016 },
issn = { 0975-8887 },
pages = { 24-29 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume133/number15/23864-2016908193/ },
doi = { 10.5120/ijca2016908193 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:31:21.203832+05:30
%A Md. Tahmid Rahman Laskar
%A Md. Tahmid Hossain
%A Abu Raihan Mostofa Kamal
%A Nafiul Rashid
%T Automated Disease Prediction System (ADPS): A User Input-based Reliable Architecture for Disease Prediction
%J International Journal of Computer Applications
%@ 0975-8887
%V 133
%N 15
%P 24-29
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Rapid proliferation of Internet technology and handheld devices has opened up new avenues for online healthcare system. There are instances where online medical help or healthcare advice is easier or faster to grasp than real world help. People often feel reluctant to go to hospital or physician on minor symptoms. However, in many cases, these minor symptoms may trigger major health hazards. As online health advice is easily reachable, it can be a great head start for users. Moreover, existing online health care systems suffer from lack of reliability and accuracy. Herein, we propose an automated disease prediction system (ADPS) that relies on guided (to be described later) user input. The system takes input from the user and provides a list (topmost diseases have greater likelihood of occurrence) of probable diseases. The accuracy of ADPS has been evaluated extensively. It ensured an average of 14.35% higher accuracy in comparison with the existing solution.

References
  1. Pew Research centre health fact sheet : www.pewinternet.org/fact-sheets/health-fact-sheet.
  2. http://edition.cnn.com/2012/05/04/tech/socialmedia/ facebook-lies-privacy [Accessed 07/06/2015]
  3. Xiaoyan Wang, Amy Chused, Nomie Elhadad, Carol Friedman, and Marianthi Markatou : “Automated Knowledge Acquisition from Clinical Narrative Reports.” , AMIA 2008 Symposium Proceedings, pp : 783-787.
  4. Nicolae Dragu, Fouad Elkhoury, Takunari Ralph and A. Morelli Nicolas di Tada : “Ontology-Based Text Mining for Predicting Disease Outbreaks.” , Proceedings of the Twenty-Third International Florida Artificial Intelligence Research Society Conference (FLAIRS 2010).
  5. www.isabelhealthcare.com [Accessed 12/10/2015]
  6. www.patient.co.uk [Accessed 11/10/2015]
  7. Kumar Sen, Shamsher Bahadur Patel and Dr. D. P. Shukla : “A Data Mining Technique for Prediction of Coronary Heart Disease Using Neuro-Fuzzy.” , International Journal Of Engineering And Computer Science ISSN 2319-7242 Volume 2 Issue 9 Sept, 2013 , pp : 2663-2671.
  8. Saba Bashir, Usman Qamar, Farhan Hassan Khan: “ BagMOOV: A novel ensemble for heart disease prediction bootstrap aggregation with multi-objective optimized voting Received.”
  9. Slav Petrov, Dipanjan Das and Ryan McDonald: “A Universal Part of-Speech Tagset.”, Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC 2012).
  10. www.mayoclinic.org [Accessed 17/10/2015]
  11. www.symptomchecker.isabelhealthcare.com [Accessed 30/10/2015]
  12. www.bettermedicine.com [Accessed 15/10/2015]
  13. http://www.kiranreddys.com/articles/clinicaldiagnosis supportsystems.pdf [Accessed 30/10/2015]
  14. Data Mining Concepts and Techniques, Third Edition: Jiawei Han, University of Illinois at Urbana–Champaign and Micheline Kamber Jian Pei, Simon Fraser University.
  15. Patrick Ernst, Cynthia Meng, Amy Siu, Gerhard Weikum : “KnowLife: a Knowledge Graph for Health and Life Sciences.” 30th International Conference on Data Engineering (ICDE), 2014 IEEE, pp : 1254 - 1257.
  16. www.webmd.com [Accessed 22/10/2015]
  17. Mount Adora Hospital & Diagnostic Center, Mirboxtula, Nayashark, Sylhet-3100.
  18. http://patient.info/forums [Accessed 27/10/2015]
  19. Samaneh Moghaddam, Martin Ester: “Aspect-based opinion mining from product reviews.”, The 35th International ACM SIGIR conference on research and development in Information Retrieval, SIGIR '12, Portland, OR, USA, August 12-16, 2012.
  20. Amit X. Garg, MD; Neill K. J. Adhikari, MD; Heather McDonald, MSc; M. Patricia Rosas-Arellano, MD, PhD; P. J. Devereaux, MD; Joseph Beyene, PhD; Justina Sam, BHSc; R. Brian Haynes, MD, PhD: “Effects of Computerized Clinical Decision Support Systems on Practitioner Performance and Patient Outcomes”, JAMA. 2005;293(10):1223-1238.doi:10.1001/jama.293.10.1223.
Index Terms

Computer Science
Information Sciences

Keywords

Relevant Attribute (RA) Data Structure Word Tagging Synonym Parent Tree Reference Tag Decision Tree.