Open AccessA Proposed Method for Mining Breast Cancer Pattern Using Particle Swarm Optimization
Author(s) -
Pranjali Dewangan,
Neelamsahu
Publication year - 2019
Publication title -
asian journal of computer science and technology
Language(s) - English
Resource type - Journals
eISSN - 2583-7907
pISSN - 2249-0701
DOI - 10.51983/ajcst-2019.8.1.2116
Subject(s) - particle swarm optimization , robustness (evolution) , data mining , computer science , breast cancer , sensitivity (control systems) , swarm behaviour , algorithm , mathematical optimization , artificial intelligence , cancer , mathematics , engineering , medicine , electronic engineering , gene , biochemistry , chemistry
Breast cancer is one of the leading causes of death among women in many parts of the world. In this paper, we have developed an efficient hybrid data mining approach to separate from a population of patients who have and who do not have breast cancer. The proposed data mining approach has consisted of two phases. In first phase, the statistical method will be used to pre-process the data, which can eliminate the insignificant features. It can reduce the computational complexity and speed up the data mining process. In the second phase, we proposed a new data mining methodology, which based on the fundamental concept of the standard particle swarm optimization (PSO), namely discrete PSO. This phase aimed at creating a novel PSO in which each particle was coded in positive integer numbers and had a feasible system structure. Based on the obtained results, our proposed DPSO can improve the accuracy to 98.71%, sensitivity to 100%, and specificity to 98.21%. When compared with the previous research, the proposed hybrid approach shows the improvement in both accuracy and robustness. According to the high quality of our research results, the proposed DPSO data mining algorithm can be used as the reference for deciding on hospital and provide the reference for the researchers.