Mobile Expert System on Febrile Diseases
||International Journal of Computer & Organization Trends (IJCOT)||
|© 2017 by IJCOT Journal|
|Volume - 7 Issue - 4
|Year of Publication : 2017|
|Authors : Alu E. S., Aniobi D. E., Ijah S. T.|
|DOI : 10.14445/22492593/IJCOT-V44P301|
Alu E. S., Aniobi D. E., Ijah S. T. "Mobile Expert System on Febrile Diseases", International Journal of Computer & organization Trends (IJCOT), V7(4):1-11 Jul - Aug 2017, ISSN:2249-2593, www.ijcotjournal.org. Published by Seventh Sense Research Group.
Abstract It is obvious that in most developing countries patients lack access to good medical services and facilities; the long queues of patients in the hospitals waiting to be attended to cannot be avoided leading to waste of time and worsening of patient’s ailment. Febrile diseases have been considered as one of the dangerous diseases which kills more people more than the dreaded Acquired Immune Deficiency Syndrome(AIDS) not because it is not curable but because of the similarities in signs and symptoms that makes it difficult for health workers and the general public to easily identify their variations and administer proper medication. This paper Mobile Expert system on Fevers, is a mobile expert appdeveloped for use in the diagnosis of many febrile diseases which includesmalaria, scarlet, typhoid, chikungunya, rheumatic, dengue, lassa, meningitis, filariasis and influenza fevers. The software was implemented using Android SDK programming language and SQLite as the database containing experts’ knowledge. It is accessible via mobile devices running on Android operating system platform, and as a data-driven app, the data supplied as symptoms by the user leads to conclusion on any of the diseases within its domain. It also provided general information to people about the ten types of fevers covered in this work. The app does not need any special requirement for its operation.
 Utzinger, J., Yesim, T., Burton, S. H. (2001) Efficacy and Cost-effectiveness of Environmental Management for Malaria Control, Tropical Medicine & International Health, 6(2001), 677-687.
 Adehor A. B., and Burrell P. R. (2008). The Integrated Management of Health Care Strategies and Differential Diagnosis by Expert System Technology: A Single-Dimensional Approach. World Academy of Science, Engineering and Technology 44 pp. 533- 538
Neeru Pathania, Ritika, Sanjeev (2016) Diagnose System for Heart Risk with Fuzzy Controller, International Journal of Computer & organization Trends (IJCOT), V31(1):1-4 www.ijcotjournal.org.
 Basil, Y. (2012). Expert Pc Troubleshooter with Fuzzy-Logic And Self Learning Support.
 Gath, S. J. and Kulkarni, R. V. (2012). A Review: Expert System for Diagnosis of Myocardial Infarction. International Journal of Computer Science and Information Technologies (IJCSIT),3(6)
 Aniobi D.E and Benisemeni Z.E. (2015). Essentials of Artificial Intelligence. Makurdi: Creativity prints media pp. 2.
 Nasuti F.W. ( 2000). Knowledge Acquisition Using Multiple Domain Experts InThe Design and Development of An Expert System For Disaster Recovery Planning Doctoral Thesis Proposal, Nova Southeastern University.
 Patkar, M.K., and Kulkami, R.V. (2013). Research Review of Expert Systems for Newborns.
 Seghal, U. (2012). Introduction to Expert System.
 Shortliffe, E. & Buchanan, B., (1994). A Knowledge Base Engineering Tool for Constructing Rule-Base Expert System. Expert system from prototype to Product. narosa publishing house
 Yan, H., Jiang, Y., Zheng, J., Peng, C. and Li, Q. (2006). A multilayer perception-based medical decision support system for heart disease diagnosis, Expert Systems with Applications, 30, 272-281.
 Devlin, H., and Devlin, J.K, (2007). Decision support system in patient diagnosis and treatment. FutureRheumatology, 2, 261-263.
 Thomson, R.G., Eccles, M.P., Steen, N.I., Greenaway, J., Stobbart, L., Murtagh, M.L., May, C.R., (2007). A patient decision aid to support shared decision-making on anti-thrombotic treatment of patients with a trial fibrillation: Randomized controlled trial. Quality and Safety in Health Care, 16, 216-223.
 Pietka, J. (2008). A preliminary study of expert systems to support a patient’s decision in thediagnosis of selected blood circulatory and respiratory systems’ diseases. Journal if Biocyberneticsand Biomedical Engineering, 28, 65-73.
 Djam, X. Y., Wajiga, G. M., Kimbi, Y. H.and Blamah, N. V. (2011). A Fuzzy Expert System for the Management of Malaria. International Journal of Pure Apply Science Technology, 5(2), pp. 84-108.
 Adebayo O. A, Fatunke M., Nwankwo U., Odiete O. G. (2013). The Design and Creation of a Malaria Diagnosing Expert System. COMPUSOFT, An international journal of advanced computer technology, 2 (12) pp. 442-449.
 Adewole, K.S, Hambali, M. A & Jimoh M. K (2015). Rule Based Expert System for Diseases Dignosis. Book of Proceedings, International Science, Technology, Engineering, Arts, Management and social Sciences (iSTREAMS) Multidisciplinary Conference. Longe, O. B., Jimoh R. G. and Ebem D. U. (Eds), 7, 183 – 190.
 Booch, G., Rumbaugh, J., Jacobson, I. (1998). The Unified Modeling Language User Guide. Addison Wesley Longman, Inc.
Artificial Intelligence, Expert System, Acute, Chronic, Bio-information, Pyrexia, Diagnoses.