An experimental comparison of fuzzy logic and analytic hierarchy process for medical decision support systems

Uzoka, Faith-Michael E.; Obot, Okure U.; Barker, Ken; Osuji, Joseph

doi:10.1016/j.cmpb.2010.06.003

Cited by 53 publications

(23 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Son yıllarda, bulanık mantık tabanlı yöntemler belirsizlik ve nesnelliğin olduğu durumlarda, mühendislik, tıp ve biyoloji alanlarında olduğu gibi tarım ve hayvancılık alanında da başarılı bir şekilde uygulanmaktadır. Tıp alanında bulanık mantık yardımıyla oluştu-rulan bulanık uzman sistemler ve karar destek sistemleri kanser ve tümör gibi ciddi hastalıkların teşhisinde ve değerlendirilmesinde karar almak amaçlı sıklıkla kullanıl-maktadır [17][18][19][20][21] . Ülkemizde oldukça yeni kullanılmaya başla-nan bulanık mantık teorisi, hayvancılık alanında da gerçekleştirilen birçok başarılı çalışmaya konu olmuştur.…”

Section: Makale Kodu (Article Code): Kvfd-2013-9894unclassified

Çiğ Süt Kalite Değerlendirmesinde Bulanık Mantık Yaklaşımı

Akıllı¹,

Atil²,

Kesenkaş³

2014

Kafkas Univ Vet Fak Derg

View full text Add to dashboard Cite

ÖzetGelişen toplum yapısı ile birlikte gerçek hayatta yaşanılan sorunlar ve olaylara bakış açıları da değişmektedir. İnsanlar sorunlarını sahip oldukları sözel ve sayısal verileri kullanarak çözmekte ve bunun için çeşitli yöntemlerden yararlanmaktadırlar. Matematiksel yöntemler insanlara kesinlik içeren durumlarda sorunların çözümlenmesinde sayısal verileri analiz ederek yardımcı olurken, belirsizlik içeren durumlarda yetersiz kalabilmektedir. Son yıllarda kalite değerlendirilmesi gibi belirsizlik içeren durumlarda ortaya çıkan problemlerin çözümünde sıklıkla kullanılan bulanık mantık, yapay zeka yöntemlerinden bir tanesidir. Klasik mantık teorisine göre daha esnek bir yapıya sahip olan bulanık mantık teorisi, olayları nesnelere "0" ve "1" arasında atadığı doğruluk dereceleri ile açıklamakta böylece sözel ve sayısal veriler arasında bir bağ oluşturmaktadır. Bu çalışmada, çiğ süt örneklerinin kalite sınıflarına ayrılmasını amaçlayan bulanık mantık tabanlı bir karar destek sistemi geliştirilmiştir. Sistemin girdileri çiğ süt örneklerine ilişkin toplam bakteri sayısı, somatik hücre sayısı ve protein miktarlarının ölçülen değerleridir. Tasarlanan bulanık sistemin çıktısı ise çiğ süt kalite değerlendirmesi şeklindedir. Yapılan analizin başarısını belirlemek amacıyla uzman kararları ile karşılaştırma yapılmış ve sistemin %80 değerinde başarılı olduğu görülmüştür. Sistemin modellenmesi Matlab (sürüm R2010b) programı kullanılarak yapılmıştır. Anahtar sözcükler: Bulanık mantık, Karar destek sistemi, Çiğ süt kalitesi Fuzzy Logic Approach in the Evaluation of Raw Milk Quality SummaryThe problems that faced with in real life and perspective of the events change with developing structure of society. The people in the face of problem use a variety of methods with their verbal and numerical data to find solution. Mathematical methods that including precision are sufficient in the analyses of numerical data while the modeling of verbal data may be insufficient in case of uncertainty. In recent years, fuzzy logic is one of the artificial intelligence methods that used in solution of the problems which are rosed from quality evaluation situations that consists of uncertainty cases. The fuzzy logic theory that has more flexible structure than the theory of classical logic, describe the events with degree of accuracy which is between "0" and "1" appointed to object. Fuzzy logic-based decision support system offers to people a more realistic and objective perspective in decision making. In this study, fuzzy logic base decision support system which aims to classify raw milk samples in quality has been developed. System inputs are; bacteria count for milk samples, somatic cell count and values for measured protein amounts. Designed fuzzy logic output is consist of raw milk quality value measurement; in order to calculate the success of the analysis, results have been compared to specialist's decisions and due to the comparison, it noticed that the system has 80% success rate. Modeling of the system has been made via Matlab (version R2...

show abstract

Section: Makale Kodu (Article Code): Kvfd-2013-9894unclassified

Çiğ Süt Kalite Değerlendirmesinde Bulanık Mantık Yaklaşımı

Akıllı¹,

Atil²,

Kesenkaş³

2014

Kafkas Univ Vet Fak Derg

View full text Add to dashboard Cite

show abstract

“…While the aforementioned medical data can be easily captured in a database, the intelligent system needs to be able to learn the hidden information in an effective way, such that it can provides useful information to support the decision-making process in disease prognosis and diagnosis. Nevertheless, the task of medical decision-making is complex and includes a lot of vague and imprecise information, possibly with missing data [2].…”

Section: Introductionmentioning

confidence: 99%

“…As an example, patients may not describe their feelings precisely; doctors may not record their observations in detail, and reports from laboratories may be erroneous [2]. As such, data-based intelligent learning systems that can learn and improve its knowledge autonomously and stably would be useful to provide the necessary decision support [3].…”

Section: Introductionmentioning

confidence: 99%

A hybrid FAM–CART model and its application to medical data classification

Seera

Lim

Tan

et al. 2015

Neural Comput & Applic

View full text Add to dashboard Cite

In this paper, a hybrid model consisting of the fuzzy ARTMAP (FAM) neural network and the classification and regression tree (CART) is formulated. FAM is useful for tackling the stability-plasticity dilemma pertaining to data-based learning systems, while CART is useful for depicting its learned knowledge explicitly in a tree structure. By combining the benefits of both models, FAM-CART is capable of learning data samples stably and, at the same time, explaining its predictions with a set of decision rules. In other words, FAM-CART possesses two important properties of an intelligent system, i.e., learning in a stable manner (by overcoming the stabilityplasticity dilemma) and extracting useful explanatory rules (by overcoming the opaqueness issue). To evaluate the usefulness of FAM-CART, six benchmark medical data sets from the UCI repository of machine learning and a real-world medical data classification problem are used for evaluation. For performance comparison, a number of performance metrics which include accuracy, specificity, sensitivity, and the area under the receiver operation characteristic curve are computed. The results are quantified with statistical indicators and compared with those reported in the literature. The outcomes positively indicate that FAM-CART is effective for undertaking data classification tasks. In addition to producing good results, it provides justifications of the predictions in the form of a decision tree so that domain users can easily understand the predictions, therefore making it a useful decision support tool.

show abstract

“…[47][48] The areas in which diversified applications are developed using fuzzy logic are: fuzzy models for illness, heart and cardiovascular disease diagnosis, asthama, abdominal pain, tropical diseases, neurological diseases, medical analogy of consumption of drugs, malaria diagnosis, diagnosis and treatment of diabetes, hepatobiliary disorder, diagnosis of male impotence, syndrome differentiation, diagnosis of lung and liver diseases, prostate diseases, lymph diseases, monitoring and control in intensive care units and operation theatres, diagnosis of chronic obstructive pulmonary diseases, diagnosis of cortical malformation, etc. [164][165][166][167][168][169][170][171][172] The non-disease areas of applications are found to be in: x-ray mammography, interpretation of mammographic and ultrasound images, electrographic investigation of human body. [72][73][74] The fuzzy expert system for different sounds produced by different organs in the human body using fuzzy logic toolbox of MATLAB has also been reported.…”

Section: -33mentioning

confidence: 99%

Fuzzy Expert Systems (FES) for Medical Diagnosis

Sikchi¹,

Sikchi²,

Ali³

2013

IJCA

View full text Add to dashboard Cite

Fuzzy logic has proved to be the remarkable tool for building intelligent decision making systems based on the expert's knowledge and observations. This paper reviews the trend in development of FES and application potential over past two and half decades in the medical field, based on the references of 173 articles from 124 journals, several proceedings and web media. In order to investigate the significance of FES for medical diagnosis, the articles are classified into five distinct categories: Reviews and Surveys on Fuzzy Expert Systems in Medical Diagnosis, Applications of Fuzzy Expert Systems in Medical Diagnosis, Methodologies and Modelling of Fuzzy Expert Systems, Neuro-Fuzzy Approaches, Fuzzy Expert System Shells and Frameworks. The development of disease specific applications using FES is observed to be the area of most significant interest of the researchers. The earlier contributions are reviewed, classified, analyzed and suggested the future scope.

show abstract

An experimental comparison of fuzzy logic and analytic hierarchy process for medical decision support systems

Cited by 53 publications

References 26 publications

Çiğ Süt Kalite Değerlendirmesinde Bulanık Mantık Yaklaşımı

Çiğ Süt Kalite Değerlendirmesinde Bulanık Mantık Yaklaşımı

A hybrid FAM–CART model and its application to medical data classification

Fuzzy Expert Systems (FES) for Medical Diagnosis

Contact Info

Product

Resources

About