Multi-Objective k-Nearest Neighbor for Breast Cancer Detection

Yessica Nataliani - Satya Wacana Christian University, Diponegoro 52-60, Salatiga, 50711, Indonesia
Christian Arthur - Brawijaya University, Ketawanggede, Kec. Lowokwaru, Malang, 65145, Indonesia
Theophilus Wellem - Satya Wacana Christian University, Diponegoro 52-60, Salatiga, 50711, Indonesia
Kristoko Hartomo - Satya Wacana Christian University, Diponegoro 52-60, Salatiga, 50711, Indonesia
Nur Wahab - Universiti Teknologi Malaysia, Jalan Iman, 81310 Skudai, Johor, Malaysia


Citation Format:



DOI: http://dx.doi.org/10.62527/joiv.9.1.2669

Abstract


Early detection of cancer is crucial. This study aims to increase the efficiency of breast cancer detection using the modified k-nearest neighbor (k-NN) algorithm. Since k-NN faces challenges with sensitivity to k values and computational complexity, a modification of k-NN was proposed, namely a multi-objective k-NN model. It was developed to incorporate multi-objective optimization and local density to create a more robust and efficient classification algorithm. The model dynamically determines the k value based on the sample density, optimizing accuracy and efficiency. Breast cancer data were collected from the University of Wisconsin Hospitals, Madison. The experimental results showed that the multi-objective k-NN model outperformed traditional k-NN and k-NN with feedback support. The proposed model achieved an accuracy of 93.7%, with precision values of 93% for the negative cancer class and 94% for the positive cancer class. These results indicate that the multi-objective k-NN model provides superior accuracy and precision in breast cancer detection, demonstrating its potential for clinical applications.


Keywords


breast cancer; classification; k-NN; multi-objective k-NN

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References


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