Much Aziz Muslim, ILKOM UNNES and S H Rukmana, , ILKOM UNNES and E Sugiharti, ILKOM UNNES and B Prasetiyo, ILKOM UNNES and S Alimah, BIOLOGI UNNES (2018) Optimization of C4.5 algorithm-based particle swarm optimization for breast cancer diagnosis. In: International Conference on Mathematics, Science and Education (ICMSE2017.

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Abstract

Data mining has become a basic methodology for computational applications in the field of medical domains. Data mining can be applied in the health field such as for diagnosis of breast cancer, heart disease, diabetes and others. Breast cancer is most common in women, with more than one million cases and nearly 600,000 deaths occurring worldwide each year. The most effective way to reduce breast cancer deaths was by early diagnosis. This study aims to determine the level of breast cancer diagnosis. This research data uses Wisconsin Breast Cancer dataset (WBC) from UCI machine learning. The method used in this research is the algorithm C4.5 and Particle Swarm Optimization (PSO) as a feature option and to optimize the algorithm. C4.5. Ten-fold cross-validation is used as a validation method and a confusion matrix. The result of this research is C4.5 algorithm. The particle swarm optimization C4.5 algorithm has increased by 0.88%.

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