A fuzzy inference- fuzzy analytic hierarchy process-based clinical decision support system for diagnosis of heart diseases

Nazari, Somayeh; Fallah, Mohammad; Kazemipoor, Hamed; Salehipour, Amir

doi:10.1016/j.eswa.2017.11.001

Cited by 116 publications

(53 citation statements)

References 13 publications

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“…FIS is an applied tool in various areas, such as risk management (Ilbahar et al, 2018;Chung et al, 2019), supplier selection (Amindoust et al, 2012;, manufacturing systems evaluation (Pourjavad and Mayorga, 2019), healthcare supply chain (Nazari et al, 2018), and so forth.…”

Section: Fuzzy Inference Systemmentioning

confidence: 99%

A decision support system for demand management in healthcare supply chains considering the epidemic outbreaks: A case study of coronavirus disease 2019 (COVID-19)

Govindan

Mina

Alavi

2020

Transportation Research Part E: Logistics and Transportation Re

515

351

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The disasters caused by epidemic outbreaks is different from other disasters due to two specific features: their long-term disruption and their increasing propagation. Not controlling such disasters brings about severe disruptions in the supply chains and communities and, thereby, irreparable losses will come into play. Coronavirus disease 2019 (COVID-19) is one of these disasters that has caused severe disruptions across the world and in many supply chains, particularly in the healthcare supply chain. Therefore, this paper, for the first time, develops a practical decision support system based on physicians' knowledge and fuzzy inference system (FIS) in order to help with the demand management in the healthcare supply chain, to reduce stress in the community, to break down the COVID-19 propagation chain, and, generally, to mitigate the epidemic outbreaks for healthcare supply chain disruptions. This approach first divides community residents into four groups based on the risk level of their immune system (namely, very sensitive, sensitive, slightly sensitive, and normal) and by two indicators of age and pre-existing diseases (such as diabetes, heart problems, or high blood pressure). Then, these individuals are classified and are required to observe the regulations of their class. Finally, the efficiency of the proposed approach was measured in the real world using the information from four users and the results showed the effectiveness and accuracy of the proposed approach.

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Section: Fuzzy Inference Systemmentioning

confidence: 99%

A decision support system for demand management in healthcare supply chains considering the epidemic outbreaks: A case study of coronavirus disease 2019 (COVID-19)

Govindan

Mina

Alavi

2020

Transportation Research Part E: Logistics and Transportation Re

515

351

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show abstract

“…The basics of AHP methodology involve structuring complex decisions as a hierarchy of goals, criteria, and alternatives, conducting a pairwise comparison of all the elements in each level of the decision hierarchy with respect to each criterion in the previous level of the hierarchy, and vertically synthesizing judgments on different levels of the hierarchy [76] AHP has been applied as a decision-making tool in many contexts due to its simplicity [77]. For instance, it was used in the evaluation and selection of medical tourism sites [78], strategic maintenance technique selection [79], heart disease diagnoses [80], critical criteria influencing the choice of arctic shipping [81], identification of potential aquaculture sites in solar saltscapes [82], ecological vulnerability assessment [83], risk assessment [84], evaluation of high-speed railway (HSR) construction projects [85], selection of suitable sites for wind power in Pakistan [86], and selection of rail system projects [87].…”

Section: A Brief Overview Of Ahpmentioning

confidence: 99%

Electric Bus Selection with Multicriteria Decision Analysis for Green Transportation

Hamurcu

Eren

2020

Sustainability

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Multicriteria decision-making tools are widely used in complex decision-making problems. There are also numerous points of decision-making in transportation. One of these decision-making points regards clean technology vehicle determination. Clean technology vehicles, such as electric buses, have some advantages compared to other technologies like internal combustion engine vehicles. Notably, electric vehicles emit zero tailpipe emissions, thereby ensuring cleaner air for cities and making these clean technologies preferable to other technologies, especially in highly populated areas for better air quality and more livable cities. In this study, we propose a multicriteria decision-making process using analytic hierarchy process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) in the context of an electric bus in the center of Ankara. Six potential electric bus alternatives were evaluated under seven specific criteria. Overall, EV-2 electric buses outperformed other electric bus alternatives based on the chosen criteria. In addition, the stability of the results obtained under different scenarios using this method was established via sensitivity analysis.

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“…AHP (Saaty, 1977(Saaty, , 1990a is an MCDA method based on ratio scales for measuring performances on considered criteria and the importance of these criteria (see Ishizaka and Labib, 2011 for a review of the main developments of AHP and Duleba and Moslem, 2019;Nazari et al, 2018 for some recent contributions related to AHP). The problem at hand is structured in AHP in a hierarchical way where the overall goal is set at the top of the hierarchy and the alternatives, being the object of the decision, are placed at the bottom of the hierarchy.…”

Section: The Analytic Hierarchy Processmentioning

confidence: 99%

A new parsimonious AHP methodology: Assigning priorities to many objects by comparing pairwise few reference objects

Abastante

Corrente

Greco

et al. 2019

Expert Systems with Applications

112

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A fuzzy inference- fuzzy analytic hierarchy process-based clinical decision support system for diagnosis of heart diseases

Cited by 116 publications

References 13 publications

A decision support system for demand management in healthcare supply chains considering the epidemic outbreaks: A case study of coronavirus disease 2019 (COVID-19)

A decision support system for demand management in healthcare supply chains considering the epidemic outbreaks: A case study of coronavirus disease 2019 (COVID-19)

Electric Bus Selection with Multicriteria Decision Analysis for Green Transportation

A new parsimonious AHP methodology: Assigning priorities to many objects by comparing pairwise few reference objects

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