A web/mobile decision support system to improve medical diagnosis using a combination of K-Mean and fuzzy logic

Al-Ars, Zainab T.; Al-Bakry, Abbas M.

doi:10.12928/telkomnika.v17i6.12715

Cited by 12 publications

(6 citation statements)

References 12 publications

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“…Scholars have shown that inference decision-making technologies of fuzzy sets have proven themselves well in diagnostic problems [ 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 ].…”

Section: Methodsmentioning

confidence: 99%

“…The main difference between such systems is that the Sugeno system produces a clear (quantitative) result in the form of a linear function value, while the Mamdani system produces a qualitative result (a fuzzy variable) [ 19 , 20 ]. If the Sugeno system is used as a mechanism for calculating a clear value, then the Mamdani algorithm can be used for linguistic analysis of the result [ 21 ].…”

Section: Methodsmentioning

confidence: 99%

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Improved Cattle Disease Diagnosis Based on Fuzzy Logic Algorithms

Mustapoevich

Tulkunovna

Safarova

et al. 2023

Sensors

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The health and productivity of animals, as well as farmers’ financial well-being, can be significantly impacted by cattle illnesses. Accurate and timely diagnosis is therefore essential for effective disease management and control. In this study, we consider the development of models and algorithms for diagnosing diseases in cattle based on Sugeno’s fuzzy inference. To achieve this goal, an analytical review of mathematical methods for diagnosing animal diseases and soft computing methods for solving classification problems was performed. Based on the clinical signs of diseases, an algorithm was proposed to build a knowledge base to diagnose diseases in cattle. This algorithm serves to increase the reliability of informative features. Based on the proposed algorithm, a program for diagnosing diseases in cattle was developed. Afterward, a computational experiment was performed. The results of the computational experiment are additional tools for decision-making on the diagnosis of a disease in cattle. Using the developed program, a Sugeno fuzzy logic model was built for diagnosing diseases in cattle. The analysis of the adequacy of the results obtained from the Sugeno fuzzy logic model was performed. The processes of solving several existing (model) classification and evaluation problems and comparing the results with several existing algorithms are considered. The results obtained enable it to be possible to promptly diagnose and perform certain therapeutic measures as well as reduce the time of data analysis and increase the efficiency of diagnosing cattle. The scientific novelty of this study is the creation of an algorithm for building a knowledge base and improving the algorithm for constructing the Sugeno fuzzy logic model for diagnosing diseases in cattle. The findings of this study can be widely used in veterinary medicine in solving the problems of diagnosing diseases in cattle and substantiating decision-making in intelligent systems.

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“…Scholars have shown that inference decision-making technologies of fuzzy sets have proven themselves well in diagnostic problems [ 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 ].…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Improved Cattle Disease Diagnosis Based on Fuzzy Logic Algorithms

Mustapoevich

Tulkunovna

Safarova

et al. 2023

Sensors

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“…According to [13], [14], identifying the usability metrics is easier than collecting them. Often, usability is measured based on users' satisfaction with a given application.…”

Section: Usability Measurementmentioning

confidence: 99%

Toward measuring the usability of decision support applications in fog computing environment

Hussein

Mousa

2023

IJEECS

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Decision support systems are applications that provide meaningful information and help with decision support. To gain the benefits of these applications, they need to be used optimally. Thus, the optimal manner to utilize relies on several properties, such as usability. This research aims to investigate the decision support systems applications (DSS) in terms of usability evaluation and propose a usability instrument for DSS in a fog computing environment. Recently, complexity is a major problem with most software products. Hence, the way to ensure user satisfaction is by measuring its usability. The study is based on the research question: What dimensions and their elements that utilize to evaluate the usability of DSS applications. A systematic literature review (SLR) was conducted to understand and extract all the instrument’s dimensions and elements. To confirm the validity of the proposed instrument, Multi scientific methods were adapted such as face validity, a pilot study for testing the goodness of proposed instrument consistency, and factor analysis. Furthermore, Bartlett’s test was utilized to ensure the reliability of the developed instrument. Eventually, the validated instrument was used to evaluate DSS application in a fog computing environment. The findings show that the proposed instrument is workable in practice.

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“…A cluster will store data based on the similarity of the data. (Al-Ars & Al-Bakry, 2019) The data that we will cluster is train passenger data for January 2023. We will cluster passenger data from business and executive classes.…”

Section: Introductionmentioning

confidence: 99%

Application of the K-Means Clustering Agorithm to Group Train Passengers in Labuhanbatu

Indah

Sari

Dar

2023

SinkrOn

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Transportation is an activity of moving things such as humans, animals, plants and goods from one place to another. To be able to implement transportation, we need a means of transportation that suits our needs. For in Indonesia, people are more inclined to land transportation. That's because land transportation already has a lot of vehicles. Land transportation already has many vehicles that can be used, both for private and for the public. Each vehicle has its uses and risks as well. Therefore we will do a data cluster from the trains. We chose the train, because the risk from using the train is very small, meaning that there is a lot of public interest in trains. So we want to do a cluster on rail passengers. The cluster that we do is to group passenger data based on the similarity of passenger data. We will do the cluster using the K-Means method. The K-Means method is very suitable when used to perform a cluster. K-Means will process widgets that are made according to the needs of the research. So after we enter the method in the widget pattern, the widget will process it to output the results from the cluster that we created. The cluster process using the K-Means method will be applied using the orange application. After we apply it, the data will later be clustered, we will cluster data as many as 3 clusters. Then the incoming data will appear in clusters 1, 2 and 3, both from business and executive classes

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A web/mobile decision support system to improve medical diagnosis using a combination of K-Mean and fuzzy logic

Cited by 12 publications

References 12 publications

Improved Cattle Disease Diagnosis Based on Fuzzy Logic Algorithms

Improved Cattle Disease Diagnosis Based on Fuzzy Logic Algorithms

Toward measuring the usability of decision support applications in fog computing environment

Application of the K-Means Clustering Agorithm to Group Train Passengers in Labuhanbatu

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