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A new expert system in prediction of lung cancer disease based on fuzzy soft sets

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Abstract

Every year, millions of people worldwide (including a major portion in China) are suffering from lung cancer disease (Chinese report of Smoking and Health 2017). The aim of this paper is to develop a new fuzzy soft expert system which can be used to predict lung cancer disease. A prediction process using this fuzzy soft expert system is composed of four main steps: (1) Transform real-valued inputs into fuzzy numbers. (2) Transform fuzzy numbers of data into fuzzy soft sets. (3) Reduce, using normal parameter reduction method, the obtained family of fuzzy soft sets into a new family of fuzzy soft sets. (4) Use the proposed algorithm to get the output data. An experiment is conducted on forty five patients (thirty males, fifteen females, all are cigarette smokers) who endure treatment in the Respiratory Department of Nanjing Chest Hospital, China. The number of training data taken was 55 records, and the remaining 45 records were used for the testing process in our system by using weight loss, shortness of breath, chest pain, persistence a cough, blood in sputum, and age of patients. The quantized accuracies of the proposed system were found to be \(100\%\). In this work, we developed a fuzzy soft expert system based on fuzzy soft sets; we used a fuzzy membership functions and an algorithm to predict those patients who may suffer lung cancer. In this way, it is possible to conclude that the use of fuzzy soft expert system can produce valuable results for lung cancer detection. It is found that the fuzzy soft expert system developed is useful to the expert doctor to decide if a patient has lung cancer or not. Finally, we introduce comparison diagnosed between our proposed system and the fuzzy inference system.

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Notes

  1. In practical problems \(A_k\in [0,1]^{Y_k}\,(Y_k\subseteq X, k\in K)\) hold since data are usually incomplete. In this paper, we identify a fuzzy set \(A\in [0,1]^Y\, (Y\subseteq X)\) with its extension \(A^*\in [0,1]^X\) which is defined by \(A^*(x)=A(x)\) (if \(x\in Y\)) or \(A^*(x)=0\) (if \(x\in X-Y\)).

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Acknowledgements

The authors are grateful to the referees for the valuable comments and suggestions. This work was supported by the National Natural Science Foundation of China under Grant Nos. 11771263 and 11641002, the Fundamental Research Funds For the Central Universities under Grant 2018CBY001, and the Fundamental for Graduate students to participate in international academic conference under Grant 2018CBY001.

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Authors and Affiliations

  1. College of Mathematics and Information Science, Shaanxi Normal University, Xi’an, 710062, People’s Republic of China

    Ahmed Mostafa Khalil & Sheng-Gang Li

  2. Department of Mathematics, Faculty of Science, Al-Azhar University, Assiut, 71524, Egypt

    Ahmed Mostafa Khalil

  3. Respiratory Department of Nanjing Chest Hospital affiliated to Southeast University, Nanjing, 210000, Jiangsu Province, People’s Republic of China

    Yong Lin

  4. School of mathematics and statistics, Long Dong University, Qingyang, 745000, People’s Republic of China

    Hong-Xia Li

  5. College of Information Science and Technology, Hainan Normal University, Haikou, 571158, People’s Republic of China

    Sheng-Guan Ma

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  1. Ahmed Mostafa Khalil
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Correspondence to Sheng-Gang Li.

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Khalil, A.M., Li, SG., Lin, Y. et al. A new expert system in prediction of lung cancer disease based on fuzzy soft sets. Soft Comput 24, 14179–14207 (2020). https://doi.org/10.1007/s00500-020-04787-x

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