Medical diagnosis system using fuzzy logic toolbox

Dagar, Preety; Jatain, Aman; Gaur, Deepti

doi:10.1109/ccaa.2015.7148370

Cited by 21 publications

(9 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Fuzzy logic is a powerful tool for representing and handling this uncertainty, leading to fuzzy systems that can support decisions in medical diagnosis. Fuzzy logic has been widely used for medical diagnosis due to its capacity to express, in a formal way, approximate concepts and reasoning, which strongly characterize the medical field [25][26][27][28][29][30][31][32][33][34][35][36][37][38]. Specifically, using fuzzy logic, the knowledge of the medical expert can be easily formalized in terms of linguistic fuzzy rules [39].…”

Section: Risk Assessment By Fuzzy Rulesmentioning

confidence: 99%

See 1 more Smart Citation

Contact-Less Real-Time Monitoring of Cardiovascular Risk Using Video Imaging and Fuzzy Inference Rules

et al. 2018

View full text Add to dashboard Cite

Conventional methods for measuring cardiovascular parameters use skin contact techniques requiring a measuring device to be worn by the user. To avoid discomfort of contact devices, camera-based techniques using photoplethysmography have been recently introduced. Nevertheless, these solutions are typically expensive and difficult to be used daily at home. In this work, we propose an innovative solution for monitoring cardiovascular parameters that is low cost and can be easily integrated within any common home environment. The proposed system is a contact-less device composed of a see-through mirror equipped with a camera that detects the person’s face and processes video frames using photoplethysmography in order to estimate the heart rate, the breath rate and the blood oxygen saturation. In addition, the color of lips is automatically detected via clustering-based color quantization. The estimated parameters are used to predict a risk of cardiovascular disease by means of fuzzy inference rules integrated in the mirror-based monitoring system. Comparing our system to a contact device in measuring vital parameters on still or slightly moving subjects, we achieve measurement errors that are within acceptable margins according to the literature. Moreover, in most cases, the response of the fuzzy rule-based system is comparable with that of the clinician in assessing a risk level of cardiovascular disease.

show abstract

Section: Risk Assessment By Fuzzy Rulesmentioning

confidence: 99%

“…Since high-frequency noise may occur in the image signals, we apply a filter based on the frequency characteristics of the heart rate to reduce the disturbance. The adopted frequency of the band-pass filter was fixed between 0.6 and 4 Hz (corresponding to heart rate [36,240]/bpm) for heart rate estimation.…”

mentioning

confidence: 99%

Contact-Less Real-Time Monitoring of Cardiovascular Risk Using Video Imaging and Fuzzy Inference Rules

et al. 2018

View full text Add to dashboard Cite

show abstract

“…According to Preety, Aman and Deepti [9], there are a number of properties in fuzzy set theory with a number of facilities that make it suitable for clinical decision support systems. Though, there are many systems for diagnoses, most of them focus on single diseases prognosis.…”

Section: Introductionmentioning

confidence: 99%

Minimizing Symptom-based Diagnostic Errors Using Weighted Input Variables and Fuzzy Logic Rules in Clinical Decision Support Systems

2021

IJATCSE

View full text Add to dashboard Cite

ThisstudycoversanapproachtowardsminimizingDiseasediagnosticerrorsusingweightedinputvariablesandFuzzyLogicruleswithmultiphasediagnosticengine.Theweightswereappliedbecausedifferentsymptomsmayhavedifferentdegreesofimportanceindifferentdiseases.Thisistoensurethatrecommendationsfordiseaseconfirmationbasedonsymptomsreturngoodpercentageoftruepositiveandtruenegatives.Thestudycreatesanenhanced,accurateandprecisesystemformedicaldiagnosisevenwhenonlythesymptomsareconsidered.Inordertoevaluatethemodel,fourcategoriesofdiagnoseswerecarriedoutwithoutusingthemodelatthefirstinstanceandusingthemodelatthesecondinstancewith50patientsdoneat4differentdiagnosticinstances.Thetruepositive(TP)andtheFalsenegativestatisticswereobtainedfromwherethefalsepositiverate(TPR)orsensitivityandfalsepositiverate(FPR)werederived.ThegraphofTPRvsFPRwasplottedfromwherethequalityofdiagnosescouldbegottenfromtheReceiverOperatingCharacteristics(ROC)space.Theresultshowsthatsensitivity,whichistheabilityofatesttocorrectlyidentifythosewiththediseaseorTruePositiveRate,andspecificity,whichistheabilityofthetesttocorrectlyidentifythosewithoutthediseasealsocalledTrueNegativeRateTNRstoodat87%and86%respectivelyusingthedevelopedmodelandthesameparameteryielded72%and56%respectivelywithoutusingthemodel.Theresultalsoshowsthatthefalsepositiverate(FPR)whichindicatesthedegreeoffalsealarmis19%usingthenewmodelwhileitis44%withoutusingthemodel.Thisresultshowsthatthelikelihoodofmakingwrongclinicaldiagnosticdecisionsismuchlowerwiththisapproach

show abstract

“…They introduced “New Hybrid Hepatitis Diagnosis System Based on Genetic Algorithm and Adaptive Network Fuzzy Inference System” [9]. Dagar et al introduced a FIS to diagnose various diseases based on initial symptoms [10]. Umoh and Ntekop proposed an expert system using the FIS to diagnose and monitor cholera [11].…”

Section: Introductionmentioning

confidence: 99%

Automated Diagnosis of Hepatitis B Using Multilayer Mamdani Fuzzy Inference System

Ahmad

Khan

Abbas

et al. 2019

Journal of Healthcare Engineering

View full text Add to dashboard Cite

In this research, a new multilayered mamdani fuzzy inference system (Ml-MFIS) is proposed to diagnose hepatitis B. The proposed automated diagnosis of hepatitis B using multilayer mamdani fuzzy inference system (ADHB-ML-MFIS) expert system can classify the different stages of hepatitis B such as no hepatitis, acute HBV, or chronic HBV. The expert system has two input variables at layer I and seven input variables at layer II. At layer I, input variables are ALT and AST that detect the output condition of the liver to be normal or to have hepatitis or infection and/or other problems. The further input variables at layer II are HBsAg, anti-HBsAg, anti-HBcAg, anti-HBcAg-IgM, HBeAg, anti-HBeAg, and HBV-DNA that determine the output condition of hepatitis such as no hepatitis, acute hepatitis, or chronic hepatitis and other reasons that arise due to enzyme vaccination or due to previous hepatitis infection. This paper presents an analysis of the results accurately using the proposed ADHB-ML-MFIS expert system to model the complex hepatitis B processes with the medical expert opinion that is collected from the Pathology Department of Shalamar Hospital, Lahore, Pakistan. The overall accuracy of the proposed ADHB-ML-MFIS expert system is 92.2%.

show abstract

Medical diagnosis system using fuzzy logic toolbox

Cited by 21 publications

References 8 publications

Contact-Less Real-Time Monitoring of Cardiovascular Risk Using Video Imaging and Fuzzy Inference Rules

Contact-Less Real-Time Monitoring of Cardiovascular Risk Using Video Imaging and Fuzzy Inference Rules

Minimizing Symptom-based Diagnostic Errors Using Weighted Input Variables and Fuzzy Logic Rules in Clinical Decision Support Systems

Automated Diagnosis of Hepatitis B Using Multilayer Mamdani Fuzzy Inference System

Contact Info

Product

Resources

About