Application of Artificial Neural Network Algorithm with Principal Component Analysis for Diagnosis of Breast Cancer Tumors Penerapan Algoritma Jaringan Syaraf Tiruan Dengan Principal Component Analysis Untuk Diagnosis Tumor Kanker Payudara
Main Article Content
Abstract
Cancer is a health disorder where abnormal cells proliferate uncontrollably and is the second leading cause of death worldwide. Breast cancer, in particular, is prevalent among women in Indonesia. This study aims to diagnose breast cancer, identifying whether it is malignant or benign, using Artificial Neural Network (ANN) algorithms to enhance the accuracy of tumor diagnosis. The fundamental principle is to develop a neural network capable of processing information efficiently without relying on Python packages such as scikit-learn. The ANN operates through forward propagation and backward propagation to optimally predict outcomes and update weights. The dataset used is from the UCI Machine Learning Repository, consisting of 569 samples and 30 features. This dataset is divided into a training set (80%) and a cross-validation set (20%). The ANN model comprises one input layer, two hidden layers, and one output layer, utilizing tanh activation functions for the hidden layers and a sigmoid activation function for the output layer. Training results indicated an accuracy of 95.6% on the training set and 93.2% on the cross-validation set. This demonstrates that the model performs well in detecting breast cancer, with a low error rate and strong generalization capability. This study successfully developed an effective and reliable ANN model for breast cancer detection with high accuracy, supporting clinical breast cancer diagnosis.
Article Details
References
[2] Global Cancer Observatory, ‘Cancer Over Time’, https://gco.iarc.fr/. Accessed: Jun. 23, 2024. [Online]. Available: https://gco.iarc.fr/
[3] National Cancer Institute, ‘Breast cancer is a disease in which malignant (cancer) cells form in the tissues of the breast.’, https://www.cancer.gov/types/breast/patient/breast-screening-pdq.
[4] M. Radak, H. Y. Lafta, and H. Fallahi, ‘Machine learning and deep learning techniques for breast cancer diagnosis and classification: a comprehensive review of medical imaging studies’, Journal of Cancer Research and Clinical Oncology, vol. 149, no. 12. 2023. doi: 10.1007/s00432-023-04956-z.
[5] R. Bro and A. K. Smilde, ‘Principal component analysis’, Analytical Methods, vol. 6, no. 9. 2014. doi: 10.1039/c3ay41907j.
[6] University of California Irvine (UCI) Machine Learning Repository., William Wolberg, Olvi Mangasarian, Nick Street, and W. Street, ‘Breast Cancer Wisconsin (Diagnostic) Data Set.’, https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic).
[7] F. Pedregosa et al., ‘Scikit-learn: Machine learning in Python’, Journal of Machine Learning Research, vol. 12, 2011.
[8] D. Learning, ‘Deep Learning - Goodfellow’, Nature, vol. 26, no. 7553, 2016.
[9] S. Suharni, ‘EKSPLORASI METODE PENGOLAHAN BIG DATA UNTUK PEMODELAN PREDIKTIF DALAM BIDANG KESEHATAN’, Jurnal Review Pendidikan dan Pengajaran (JRPP), vol. 7, no. 1 SE-Articles, 2024.
10] James Issac, ‘Understanding Deep Neural Networks from First Principles: Logistic Regression’, Medium.com, https://medium.com/@melodious/understanding-deep-neural-networks-from-first-principles-logistic-regression-bd2f01c9e263.
[11] K. P. Murphy, ‘Machine Learning - A Probabilistic Perspective - Table-of-Contents’, The MIT Press, 2012.
[12] C. M. Bishop, ‘Bishop - Pattern Recognition And Machine Learning - Springer 2006’, Antimicrob Agents Chemother, vol. 58, no. 12, 2014.
[13] I. Goodfellow, Y. Bengio, and A. Courville, Deep learning An MIT Press Book, vol. 29, no. 7553. 2016.
[14] D. P. Kingma and J. L. Ba, ‘Adam: A method for stochastic optimization’, in 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings, 2015.
[15] Y. LeCun, G. Hinton, and Y. Bengio, ‘Deep learning (2015), Y. LeCun, Y. Bengio and G. Hinton’, Nature, vol. 521, 2015.