Bimodal fuzzy analytic hierarchy process (BFAHP) for coronary heart disease risk assessment
Open accessAbstract
Rooted deeply in medical multiple criteria decision-making (MCDM), risk assessment is very important especially when applied to the risk of being affected by deadly diseases such as coronary heart disease (CHD). CHD risk assessment is a stochastic, uncertain, and highly dynamic process influenced by various known and unknown variables. In recent years, there has been a great interest in fuzzy analytic hierarchy process (FAHP), a popular methodology for dealing with uncertainty in MCDM. This paper proposes a new FAHP, bimodal fuzzy analytic hierarchy process (BFAHP) that augments two aspects of knowledge, probability and validity, to fuzzy numbers to better deal with uncertainty. In BFAHP, fuzzy validity is computed by aggregating the validities of relevant risk factors based on expert knowledge and collective intelligence. By considering both soft and statistical data, we compute the fuzzy probability of risk factors using the Bayesian formulation. In BFAHP approach, these fuzzy validities and fuzzy probabilities are used to construct a reciprocal comparison matrix. We then aggregate fuzzy probabilities and fuzzy validities in a pairwise manner for each risk factor and each alternative. BFAHP decides about being affected and not being affected by ranking of high and low risks. For evaluation, the proposed approach is applied to the risk of being affected by CHD using a real dataset of 152 patients of Iranian hospitals. Simulation results confirm that adding validity in a fuzzy manner can accrue more confidence of results and clinically useful especially in the face of incomplete information when compared with actual results. Applying the proposed BFAHP on CHD risk assessment of the dataset, it yields high accuracy rate above 85% for correct prediction. In addition, this paper recognizes that the risk factors of diastolic blood pressure in men and high-density lipoprotein in women are more important in CHD than other risk factors.
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Research works printed but not linked: 13
- C. Hwang, A. Masud, Multiple Attribute Decision Making: Methods and Applications -A State-of-the-art Survey, Springer-Verlag, Berlin, 1981.
- D. Wu, Intelligent Systems for Decision Support, PhD Dissertation, Ming Hsieh Department of Electrical Engineering, University of Southern California, Los Angeles, 2009.
- A. Blumenthal, The Process of Cognition, Prentice Hall, NJ: Englewood Cliffs, 1977.
- J.M. Mendel, Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions, first ed., Prentice Hall, 2001.
- F. Sabahi, M.R. Akbarzadeh-T, Extended Fuzzy Logic: sets and systems, IEEE Trans. Fuzzy Syst. 24 (2016) 530-543.
- Error Profile using UCI database. F. Sabahi Journal of Biomedical Informatics 83 (2018) 204-216
- M. Hanss, Applied Fuzzy Arithmetic: An Introduction with Engineering Applications, vol. 1, Springer-Verlag Berlin Heidelberg, The Netherlands, 2005.
- R. Walpole, W.R. Myers, H.A. Myers, L.K. Ye, Probability & Statistics for Engineers and Scientists vol. 9th ed., Upper Saddleback River Prentice-Hall, 2011.
- J.M. Mendel, R.I.B. John, Type-2 fuzzy sets made simple, IEEE Trans. Fuzzy Syst. 10 (2002) 117-127.
- F. Liu, J.M. Mendel, Encoding words into interval type-2 fuzzy sets using an interval approach, IEEE Trans. Fuzzy Syst. 16 (2008) 1503-1521.
- J.M. Mendel, Type-2 fuzzy sets and systems: an overview, IEEE Comput. Intell. Mag. 2 (2007) 20-29.
- J.M. Mendel, D. Wu, Perceptual Computing: Aiding People in Making Subjective Judgments, first ed., Wiley-IEEE Press, 2010.
- B.S. Rao, K.N. Rao, S.P. Setty, An approach for heart disease detection by enhancing training phase of neural network using hybrid algorithm, in: 2014 IEEE International Advance Computing Conference (IACC), 2014, pp. 1211-1220.
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Effective Heart Disease Prediction Using Hybrid Machine Learning Techniques
Mohan, S., Thirumalai, C., & Srivastava, G. (2019) · IEEE Access · doi:10.1109/access.2019.2923707 · Cited by 1,951 · see citing works
“4% accuracies in the prediction of heart disease [28], [36].”
Background
- Authors
- Sabahi, F.
- Affiliations
- Urmia University (IR)
- Corresponding author
- Sabahi, Farnaz
- ORCID
- Sabahi, Farnaz
- Venue
- Journal of Biomedical Informatics
- Publisher
- Elsevier BV
- Published
- 2018-07
- Volume
- 83
- Pages
- 204-216
- ISSN
- 1532-0464 (ISSN-L)
- OpenAlex
- W2795595258
- Cited by
- 36
- References
- 0
- Citations by year
- 2026: 3 · 2025: 2 · 2024: 4 · 2023: 1 · 2022: 10 · 2021: 5 · 2020: 8 · 2019: 2 · 2018: 1
- FWCI
- 3.19
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Journal of Biomedical Informatics (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Multi-Criteria Decision Making
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Environmental Science › Ecological Modeling › Evaluation Methods in Various Fields
- Keywords
- coronary heart disease risk assessment, fuzzy analytic hierarchy process, fuzzy probability, risk factors, clinical uncertainty, Bayesian aggregation, pairwise comparison matrix, diastolic blood pressure, high-density lipoprotein, incomplete information
- MeSH terms
- Clinical Decision-Making, Bayes Theorem, Computer Simulation, Coronary Disease, Diagnosis, Computer-Assisted, Female, Humans, Male, Risk Factors, Fuzzy Logic, Risk Assessment, Uncertainty
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