Use of Support Vector Machines to Predict the Success of Wart Treatment Methods

Uzun, Rukiye; İşler, Yalçın; Toksan, Mualla

doi:10.1109/asyu.2018.8554010

Cited by 4 publications

(4 citation statements)

References 17 publications

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“…In the literature related to prediction of the success of wart treatment method, classifier algorithms of Fuzzy Rules (Khozeimeh et al 2017b), support vector machines (Uzun et al 2018a), Naive Bayes (Uzun et al 2018b) and k-Nearest Neighbors (Uzun et al 2018b) were used previously. We explored the use of multi-layer perceptron and extremelearning machine on solving this problem.…”

Section: Resultsmentioning

confidence: 99%

“…Khozeimeh et al (2017b) prepared the database and obtained prediction accuracies of 83.3% for immunotherapy method and 80.7% for cryotherapy method. Uzun et al (2018aUzun et al ( , 2018bUzun et al ( , 2019 Putra et al (2018) proposed the AdaBoost algorithm to determine the success of the selected wart treatment method and achieved the maximum classification performance of an accuracy of 93.89%, a sensitivity of 96.64%, and a specificity of 93.10%. Khatri et…”

Section: Validation and Performance Measuresmentioning

confidence: 99%

“…Khozeimeh et al (2017b) created Fuzzy Logic-based rule tables for these two wart treatment methods and they achieved the average performance of 80%. In another study, Uzun et al (2018a) investigated the performances of these two wart treatment methods with Naive Bayes and k-nearest neighbor classifier algorithms. They found a similar classifier performance by using the 7-nearest neighbor classifiers.…”

Section: Introductionmentioning

confidence: 99%

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Uzun¹,

İşler²,

Toksan³

2020

KSEJ

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groups according to their differences about colors, sizes, and constitutional structure: Common warts, Plantar warts, Flat warts, and Genital warts. Common and plantar warts are the most common types among them. Common warts are a type of wart seen on hands, fingers, and nails. They are harmful but painless. Plantar warts are painful warts embedded in the skin in single or multiple groups (Hosrik 2010). Almost all age groups, especially children and young adults (between 10 and 20 years of age) suffer from warts. It is estimated that about 10% of the public population suffer from warts in Turkey (Carman et al. 2013, Hosrik 2010. To eliminate this type of dermatoses, several treatment methods have been developed. Surgical treatment, cryotherapy,

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Section: Resultsmentioning

confidence: 99%

Section: Validation and Performance Measuresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Untitled

Uzun¹,

İşler²,

Toksan³

2020

KSEJ

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“…For Clinical Decision Support Systems, many predictive or classification algorithms have been implemented to diagnose different diseases [15]. Among them, we can find works using fuzzy rule miner [16], Constructive Deep Neural Network [9], Support Vector Machines [17,18], ANFIS [19,20], genetic algorithms [21], random forest [22,23], Decision Trees [24,25], k-NN [24], and more. Most of these intelligent systems have their algorithms for “learning” from the data.…”

Section: Introductionmentioning

confidence: 99%

Framework for the Development of Data-Driven Mamdani-Type Fuzzy Clinical Decision Support Systems

et al. 2019

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Clinical decision support systems (CDSS) have been designed, implemented, and validated to help clinicians and practitioners for decision-making about diagnosing some diseases. Within the CDSSs, we can find Fuzzy inference systems. For the reasons above, the objective of this study was to design, to implement, and to validate a methodology for developing data-driven Mamdani-type fuzzy clinical decision support systems using clusters and pivot tables. For validating the proposed methodology, we applied our algorithms on five public datasets including Wisconsin, Coimbra breast cancer, wart treatment (Immunotherapy and cryotherapy), and caesarian section, and compared them with other related works (Literature). The results show that the Kappa Statistics and accuracies were close to 1.0% and 100%, respectively for each output variable, which shows better accuracy than some literature results. The proposed framework could be considered as a deep learning technique because it is composed of various processing layers to learn representations of data with multiple levels of abstraction.

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