Reasoning with Certainty Factor for Prediction of Diabetes Disease on Machine Learning Platform

Authors

  • M.M. Mastoli Department of Computer Science Shivaji University Kolhapur, Maharashtra, India
  • U.R. Pol Department of Computer Science Shivaji University Kolhapur, Maharashtra, India
  • Rahul D. Patil Quality Assurance Engineer, Menon Bearing Ltd, Kolhapur, Maharashtra

Keywords:

Artificial intelligence, Machine Learning Model, Certainty Factor, Expert System, Diabetes Mellitus

Abstract

Diabetes Mellitus commonly called as diabetes, is one of the common and growing endocrine diseases. AI provides ability to prevent these type of disease at an early stage by predicting the symptoms using several methods. Two areas which may benefit from the application of Machine Learning techniques in the medical field are diagnosis and outcome prediction. Expert systems as a branch of AI can incorporate with machine learning tools, this technology can be used as a solve the problem where there is a shortage of physician in the field of healthcare. The researcher has designed and developed a machine learning model for predicting the diabetes and its types like expert systems. This present reasoning with certainty factor and considered when designed the knowledge base with the patient`s level of belief. The main purpose of this paper is to study certainty factor for prediction of diabetes disease for more accurate prediction and diagnosis of diseases and application of machine learning for heath care systems.

 

References

Jordan MI, Mitchell TM. “Machine learning: Trends, perspectives, and prospects”. Sci (NY). 2015; 349(6245):255–60.

J.-J. Yang, J. Li, J. Mulder, Y. Wang, S. Chen, H. Wu, Q. Wang, and H. Pan, “Emerging information technologies for enhanced healthcare,” Comput. Ind., vol. 69, pp. 3–11, 2015.

Nilmini Wickramasinghe,Rajeev K. Bali, Brian Lehaney , Jonathan L. Schaffer , M. Chris Gibbons “Knowledge Management in Healthcare Primer” Routledge 270 Madison Ave, New York, pp 7-15,2009

Shortliffe, EH.,Perrault, LE., (Eds.). “Medical informatics: Computer applications in health care and biomedicine” (2nd Edition). New York: Springer, 2000.

M. M. Mastoli,U. R. Pol,Rahul D. Patil, "AI for Diabetic Retinopathy",ISROSET Publisher,India,International Journal of Scientific Research in Computer Science and Engineering, Vol.7, Issue.6, pp.29-34, 2019

Tawfik Saeed Zeki, Mohammad V. Malakooti, Yousef Ataeipoor, S.Talayeh Tabibi ,“An Expert System for Diabetes Diagnosis”, American Academic & Scholarly Research Journal Vol. 4, No. 5, Sept 2012

Dilip Kumar Choubey, Sanchita Paul, Vinay Kumar Dhandhenia “Rule based diagnosis system for diabetes” Biomedical Research 2017; 28 (12): 5196-5209,

Anindito Yoga Pratama, Dewi Agushinta R., and Remi Senjaya ,“Design Of Mobile Expert System For Diabetes Risk Diagnosis And Information”, Journal of Information Systems, Volume 9, Issue 1, April 2013, 32-36

M. Kalpana, Dr. A.V Senthil Kumar,“Diagnosis of Diabetes using Correlation fuzzy logic in Fuzzy Expert System”, International Journal of Advanced Research in Computer Science, 3 (1), Jan –Feb, 2012,244-250

D., Tadić, P., Popović, A., Đukić,“A Fuzzy Approach to Evaluation” Yugoslav Journal of Operations Research Volume 20 (2010), Number 1, 99-116 , 10.2298/YJOR1001099T

Hanslal Prajapati1, Anurag Jain2, Sanjay Kumar Pal3 “An Enhance Expert System for Diagnosis of Diabetes using Fuzzy Rules over PIMA Dataset”, International Journal of Advance Engineering and Research Development (IJAERD) Volume 4, Issue 9, September-2017, e-ISSN: 2348 - 4470, print-ISSN: 2348-6406

Abdulla Al-Malaise Al-Ghamdi et al, “An Expert System of Determining Diabetes Treatment Based on Cloud Computing Platforms”, (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 2 (5) , 2011, 1982-1987

Alfonese, Marco et al. “Daily Meal Planner Expert System for Diabetics Type-2.” (2015).

Seyedeh Talayeh Tabibi , Tawfik Saeed Zaki , Yousef Ataeepoor,“ Developing an Expert System for Diabetics Treatment Advices”, International Journal of Hospital Research 2013, 2(3):155-162

Meysam Rahmani Katigari, Haleh Ayatollahi, Mojtaba Malek, and Mehran Kamkar Haghighi, “Fuzzy expert system for diagnosing diabetic neuropathy”, World J Diabetes. 2017 Feb 15; 8(2): 80–88.

A.D.Dhivya , A.Felix ,“A Fuzzy Rule based Expert System for T2DM Diagnosis” International Journal of Engineering & Technology, 7 (4.10) (2018) 432-435

K.Vijaya Lakshmi,E.Sreedevi, Prof.M.Padmavathamma “Modeling An Expert System For Diagnosis Of Gestational Diabetes Mellitus Based On Risk Factors” Publications Of Problems & Application In Engineering Research – Paper, Vol 04, Special Issue01; 2013

Onuiri Ernest E , Ndukwe Victor U , Igwe Nkechinyere , Olise Saviour C. “Simulation of an Expert System for Diabetes Diagnosis” International Journal of Advanced Computing, ISSN:2051-0845, Vol.48, Issue.1 1614-1621

R. Radha and S.P. Rajagopalan,“Fuzzy Logic Approach for Diagnosis of Diabetics”, Information Technology Journal Volume 6 (1): 96-102, 2007

Vishali Bhandari, Rajeev Kumar ,“Comparative Analysis of Fuzzy Expert Systems for Diabetic Diagnosis”, International Journal of Computer Applications Volume 132 - Number 6.

Tb. Ai Munandar , Suherman,Sumiati, “The Use of Certainty Factor with Multiple Rules for Diagnosing Internal Disease”, International Journal of Application or Innovation in Engineering & Management (IJAIEM), Volume 1, Issue 1, September 2012 ISSN 2319 – 4847

M.M.Mastoli,U.R.Pol,R.D.Patil,“Machine Learning Model For Prediction of Diabetes Mellitus”, International Journal of Recent Technology and Engineering, Blue Eyes Intelligence Engineering & Sciences Publication, Volume-8 Issue-5, January 2020

M.M.Mastoli,U.R.Pol,R.D.Patil,“Machine Learning Classification Algorithms for Predictive Analysis in Healthcare”, International Research Journal of Engineering and Technology, Volume-6 Issue-12, pp.1225-1229,2019.

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Published

2020-02-28

How to Cite

[1]
M. Mastoli, U. Pol, and R. D. Patil, “Reasoning with Certainty Factor for Prediction of Diabetes Disease on Machine Learning Platform”, Int. J. Sci. Res. Comp. Sci. Eng., vol. 8, no. 1, pp. .93–97, Feb. 2020.

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Section

Research Article

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