Application of soft sets to diagnose the prostate cancer risk

Saziye Yuksel,Unal Sert,Gulnur Yildizdan,Tugbahan Dizman

doi:10.1186/1029-242x-2013-229

Saziye Yuksel, Unal Sert + Show 2 more

Open Access

https://doi.org/10.1186/1029-242x-2013-229

Copy DOI

Abstract

Abstract In recent years the artificial intelligence has been developed rapidly since it can be applied easily to several areas like medical diagnosis, engineering and economics, among others. In this study we have devised a soft expert system (SES) as a prediction system for prostate cancer by using the prostate specific antigen (PSA), prostate volume (PV) and age factors of patients based on fuzzy sets and soft sets and have calculated the patients’ prostate cancer risk. Our data set has been provided by the Department of Urology, Meram Medical Faculty in Necmettin Erbakan University, Konya, Turkey.

Highlights

In recent years vague concepts have been used in different areas such as medical applications, pharmacology, economics and engineering since the classical mathematics methods are inadequate to solve many complex problems in these areas
He showed that soft set theory is free from the parametrization inadequacy syndrome of other theories developed for vagueness
To address decision making problems based on fuzzy soft sets, Feng et al introduced the concept of level soft sets of fuzzy soft sets and initiated an adjustable decision-making scheme using fuzzy soft sets [ ]

Summary

Introduction

In recent years vague concepts have been used in different areas such as medical applications, pharmacology, economics and engineering since the classical mathematics methods are inadequate to solve many complex problems in these areas. The fuzzy set theory has become very popular and has been used to solve problems in different areas. There exists a difficulty: how to set the membership function in each particular case The reason for these difficulties is, possibly, the inadequacy of a parametrization tool of the theory [ ]. Soft set theory was initiated by Molodtsov [ ] as a new method for vagueness. Molodtsov showed in his paper that the theory can be applied to several areas successfully; for example, the smoothness of functions, game theory, Riemann-integration, Perron-integration, etc He showed that soft set theory is free from the parametrization inadequacy syndrome of other theories developed for vagueness. Maji et al [ ] defined a hybrid model called fuzzy soft sets. Simsekler (Dizman) and Yuksel [ ] contributed to fuzzy soft topological structures

Objectives

Conclusion