Yessica Nataliani, Christian Arthur, Theophilus Wellem, Kristoko Dwi Hartomo, Nur Haliza Abdul Wahab
Early detection of cancer is crucial. This study aims to increase the efficiency of breast cancer detection by 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. Future research should focus on optimizing the model using multi-objective optimization techniques and adding features to improve diagnostic accuracy. Testing it in real-world clinical settings will help confirm its effectiveness. Exploring hybrid models that combine multi-objective k-NN with other machine learning methods, like deep learning or ensemble approaches, could enhance performance. Practical applications include early detection systems, clinical decision support, and personalized treatment plans. Implementing this model in clinical practice could significantly improve early breast cancer detection, resulting in better patient outcomes and higher survival rates. © 2025, Politeknik Negeri Padang. All rights reserved.
Faculty of Information Technology, Satya Wacana Christian University, Diponegoro, Salatiga, Indonesia; Department of Computer Science, Brawijaya University, Ketawanggede, Kec. Lowokwaru, Malang, Indonesia; Faculty of Computing, Universiti Teknologi Malaysia, Johor, Skudai, Malaysia
