Fuzzy expert system for predicting pathological stage of prostate cancer

Castanho, M.J.P.; Hernandes, Fábio; Ré, Angelita Maria de; Rautenberg, Sandro; Billis, Athanase

doi:10.1016/j.eswa.2012.07.046

Cited by 47 publications

(26 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…ANNs have many advantages such as, flexible modeling structure for large data sets and highly accurate results that support clinical decision-making [29][30][31]. ANNs model was applied by some researchers in the diagnosis of thyroid disease [20-22, 24, 28, 48, and, 32, 33] and other diseases including diagnosis of tuberculosis [34], schizophrenia [36], anemia in children [37], heart disease [38] and, predicting prostate cancer [35]. Also, ANN S have been used by Lahner et al (2008) in the recognition of the presence of thyroid disease in patients with atrophic body gastritis [39].…”

Section: Introductionmentioning

confidence: 99%

Correlation between drinking water fluoride and TSH hormone by ANNs and ANFIS

Kheradpisheh

Mahvi

Mirzaei

et al. 2018

J Environ Health Sci Engineer

View full text Add to dashboard Cite

Background Artificial neural networks (ANNs) and adaptive neural-fuzzy Inference system (ANFIS) are the best solutions to finding the correlation between some water parameters and human hormones. The correlation between thyroid stimulating hormone (TSH) and drinking water fluoride studied by ANN S and ANFIS models in Yazd city. Method In this study, eighty people with thyroid gland disorder and 213 healthy people invited. Their thyroid hormones and fluoride drinking water analyzed. Results The result of ANFIS showed R 2 = 0.81 for test and R 2 = 0.85 for train in all cases and controls data. This results were R 2 = 0.73 and R 2 = 0.81 for ANNs respectively. Conclusion This models can be used as an alternative for show correlation between Drinking Water Fluoride and TSH Hormone and R 2 = 0.85 gained from ANFIS was the best.

show abstract

Section: Introductionmentioning

confidence: 99%

Correlation between drinking water fluoride and TSH hormone by ANNs and ANFIS

Kheradpisheh

Mahvi

Mirzaei

et al. 2018

J Environ Health Sci Engineer

View full text Add to dashboard Cite

show abstract

“…y * = any point in hgt (R) (5) where hgt (R) refers to the set of all points reaching the maximum value among μR (y).…”

Section: Defuzzificationmentioning

confidence: 99%

“…The research has been developed for detecting breast cancer [3] and typhoid fever [4] which produce nearly 84% and 94% accuracy using fuzzy logic. In the medical problem, fuzzy expert system method can also be used for the prostate cancer [5], classification of anti-tuberculosis [6], predicting of Mycobacterium Tuberculosis [7], expert systems for lung tuberculosis [8] and decision support system for tuberculosis [9] and also classification of chronic kidney disease [10]. Fuzzy logic can also be combined with other methods of Artificial Intelligence, i.e.…”

Section: Introductionmentioning

confidence: 99%

A comparative study on fuzzy Mamdani-Sugeno-Tsukamoto for the childhood tuberculosis diagnosis

Sari

Wahyunggoro

Fauziati

2016

AIP Conference Proceedings

View full text Add to dashboard Cite

Abstract. World Health Organization (WHO) estimated that approximately 80 thousand children died every year in view of Childhood Tuberculosis. The disease needs an appropriate treatment considering the difficulties in establishing a diagnosis in pediatric patients. The incapability of children to produce sputum becomes one of the difficulties. Sputum is used to diagnose a person suffering from tuberculosis, based on Mycobacterium tuberculosis in sputum. In this paper, Mamdani, Tsukamoto and Sugeno-types Fuzzy Inference System are applied to assist the tuberculosis diagnosis. The different technique in these three methods is aimed to determine the most appropriate method for such diagnosis. The results show that, of the three types of Fuzzy Inference System, the best model is Sugeno model. Sugeno-type FIS has a better accuracy compared to both Mamdani and Tsukamoto ones at 93%, equivalent to a fault diagnosis in 13 of 180 patients. Here, Mamdani-type FIS is provided the diagnostic accuracy of 89%, equivalent to the fault diagnosis in 20 of 180 patients. On the other hand, Tsukamoto is provided the diagnostic accuracy of 92%, equivalent to fault diagnosis in 15 of 180 patients. Based on the three systems, the most precise output is found in Sugeno-type Fuzzy with a value by 95.1% while for Fuzzy Mamdani and Tsukamoto, it values are 93.4% and 94.5%, respectively. Also, the highest level for the system sensitivity is found in Sugeno with 97.2% in comparison to Tsukamoto FIS by 96.67% and Mamdani at 94.4%.

show abstract

“…[18] presents a fuzzy application to analyze diabetic state. In another hand, using fuzzy logic many systems take decision: [19] about hypertension state, [20] about lever state, [21] about state of prostate cancer, [22] about heart state, [23] about breast cancer. …”

Section: Fuzzy Expert System Overviewmentioning

confidence: 99%

“…Publications about SEF Industrial diagnosis [45], [30], [31], [32], [33], [28], [34], [35], [36], [37], [38], [39], [40], [41], [42], [6], [43] , [44], [7], [11], [14], [8], [15], [10], [50], [58] Medical diagnosis [47], [52], [46] , [48], [49] , [18], [19], [20], [21], [53], [23], [54], [19], [45] Video surveillance [54], [60] Economic domain [55], [56], [51] Civil domain [34], [10], [27] Software domain [57] 219 | P a g e www.ijacsa.thesai.org This state of art on FES between 2007 and 2013 took into account 50 n...…”

Section: Fes Application Fieldsmentioning

confidence: 99%

A Study on the Conception of Generic Fuzzy Expert System for Surveillance

Yousra¹,

Ketata²,

Ksouri³

2013

IJACSA

View full text Add to dashboard Cite

Abstract-This paper deals with using fuzzy logic to minimize uncertainty effects in surveillance. It studies the conception of an efficient fuzzy expert system that had two characteristics: generic and robust to uncertainties. Analyzing distance between variables optimal and real values is the main idea of the research. Fuzzy inference system decides, then, about significant variables state: normal or abnormal. A comparison between three proposed fuzzy expert systems is presented to highlight the effect of membership number and type. Beside, being generic this system could also be applied in three fields: industrial surveillance, camera surveillance and medical surveillance. To expose results in these fields, matlab is used to realize this approach and to simulate systems responses which revealed interested conclusions.

show abstract

Fuzzy expert system for predicting pathological stage of prostate cancer

Cited by 47 publications

References 31 publications

Correlation between drinking water fluoride and TSH hormone by ANNs and ANFIS

Correlation between drinking water fluoride and TSH hormone by ANNs and ANFIS

A comparative study on fuzzy Mamdani-Sugeno-Tsukamoto for the childhood tuberculosis diagnosis

A Study on the Conception of Generic Fuzzy Expert System for Surveillance

Contact Info

Product

Resources

About