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Penelitian ini menganalisis kinerja algoritma Random Forest dan Logistic Regression dalam mendeteksi kanker payudara menggunakan dataset dari Kaggle. Evaluasi dilakukan berdasarkan metrik seperti akurasi, precision, recall, dan F1-score untuk mengklasifikasikan kanker jinak (benign) dan ganas (malignant). Logistic Regression mencatat akurasi 98%, dengan precision masing-masing 99% untuk kelas jinak dan 98% untuk kelas ganas, serta recall 99% untuk kedua kelas. Sementara itu, Random Forest menunjukkan akurasi 96%, precision 96% untuk kelas jinak dan 98% untuk kelas ganas, serta recall sebesar 99% untuk kelas jinak dan 93% untuk kelas ganas. Penelitian ini memberikan kontribusi dengan menyoroti keunggulan Logistic Regression dalam menghasilkan hasil yang lebih akurat dan konsisten pada dataset sederhana, sementara Random Forest menunjukkan potensi lebih besar dalam menangani data dengan pola yang lebih kompleks. Berbeda dari studi sebelumnya, penelitian ini menekankan pentingnya mencocokkan karakteristik dataset dengan algoritma yang dipilih untuk meningkatkan akurasi deteksi dini kanker payudara. Hasil ini diharapkan dapat mendukung pengambilan keputusan berbasis bukti di bidang klinis, terutama dalam memilih algoritma yang paling sesuai dengan kebutuhan dan keterbatasan sumber daya.
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References
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References
S. F. Khorshid, A. M. Abdulazeez, and A. B. Sallow, “A Comparative Analysis and Predicting for Breast Cancer Detection Based on Data Mining Models,” Asian J. Res. Comput. Sci., no. May, pp. 45–59, 2021, doi: 10.9734/ajrcos/2021/v8i430209.
Y. Amethiya, P. Pipariya, S. Patel, and M. Shah, “Comparative analysis of breast cancer detection using machine learning and biosensors,” Intell. Med., vol. 2, no. 2, pp. 69–81, 2022, doi: 10.1016/j.imed.2021.08.004.
Z. Khandezamin, M. Naderan, and M. J. Rashti, “Detection and classification of breast cancer using logistic regression feature selection and GMDH classifier,” J. Biomed. Inform., vol. 111, no. October, p. 103591, 2020, doi: 10.1016/j.jbi.2020.103591.
H. Jumakhan and A. Mirzaeinia, “Comparative Analysis of Kolmogorov-Arnold Networks and Traditional Machine Learning Models for Breast Cancer Prognosis Comparative Analysis of Kolmogorov-Arnold Networks and Traditional Machine Learning Models for Breast Cancer Prognosis,” no. November, 2024.
Y. Yan et al., “An Oversampling - enhanced Multi - class Imbalanced Classification Framework for Patient Health Status Prediction Using Patient - reported Outcomes,” pp. 1–10.
I. D. Mienye and Y. Sun, “Performance analysis of cost-sensitive learning methods with application to imbalanced medical data,” Informatics Med. Unlocked, vol. 25, p. 100690, 2021, doi: 10.1016/j.imu.2021.100690.
D. T. E. Mathew, “An Improvised Random Forest Model for Breast Cancer Classification,” NeuroQuantology, vol. 20, no. 5, pp. 713–722, 2022, doi: 10.14704/nq.2022.20.5.nq22227.
K. V Shiny, A. K. Ajnabi, A. Kumar, B. K. Singh, and A. Gupta, “A Machine Learning Approach for Breast Cancer Detection using Random Forest Algorithm,” vol. 7, no. 4, pp. 14–18, 2024.
R. L. Negrut, A. Cote, V. A. Caus, and A. M. Maghiar, “Systematic Review and Meta-Analysis of Laparoscopic versus Robotic-Assisted Surgery for Colon Cancer: Efficacy, Safety, and Outcomes—A Focus on Studies from 2020–2024,” Cancers (Basel)., vol. 16, no. 8, 2024, doi: 10.3390/cancers16081552.
M. Monirujjaman Khan et al., “Machine Learning Based Comparative Analysis for Breast Cancer Prediction,” J. Healthc. Eng., vol. 2022, 2022, doi: 10.1155/2022/4365855.
L. Muflikhah, F. A. Bachtiar, D. E. Ratnawati, and R. Darmawan, “Improving Performance for Diabetic Nephropathy Detection Using Adaptive Synthetic Sampling Data in Ensemble Method of Machine Learning Algorithms,” J. Ilm. Tek. Elektro Komput. dan Inform., vol. 10, no. 1, p. 123, 2024, doi: 10.26555/jiteki.v10i1.28107.
N. Fatima, L. Liu, S. Hong, and H. Ahmed, “Prediction of Breast Cancer, Comparative Review of Machine Learning Techniques, and Their Analysis,” IEEE Access, vol. 8, pp. 150360–150376, 2020, doi: 10.1109/ACCESS.2020.3016715.
A. Raheem, S. Waheed, M. Karim, N. U. Khan, and R. Jawed, “Prediction of major adverse cardiac events in the emergency department using an artificial neural network with a systematic grid search,” Int. J. Emerg. Med., vol. 17, no. 1, pp. 1–11, 2024, doi: 10.1186/s12245-023-00573-2.
T. E. Mathew and K. S. Anil Kumar, “A logistic regression based hybrid model for breast cancer classification,” Indian J. Comput. Sci. Eng., vol. 11, no. 6, pp. 899–906, 2020, doi: 10.21817/indjcse/2020/v11i6/201106201.
H. Chen, N. Wang, X. Du, K. Mei, Y. Zhou, and G. Cai, “Classification Prediction of Breast Cancer Based on Machine Learning,” vol. 2023, 2023, doi: 10.1155/2023/6530719.
G. Magesh and P. Swarnalatha, “Analysis of breast cancer prediction and visualisation using machine learning models,” Int. J. Cloud Comput., vol. 11, no. 1, pp. 43–60, 2022, doi: 10.1504/IJCC.2022.121075.
S. Sasidharan Nair and M. Subaji, “Automated Identification of Breast Cancer Type Using Novel Multipath Transfer Learning and Ensemble of Classifier,” IEEE Access, vol. 12, no. May, pp. 87560–87578, 2024, doi: 10.1109/ACCESS.2024.3415482.