Pendekatan Machine Learning yang Efisien untuk Prediksi Kanker Payudara

Breast Cancer is the most common cancer found in women and the death rate is still in second place among other cancers. The high accuracy of the machine learning approach that has been proposed by related studies is often achieved. However, without efficient pre-processing, the model of Breast Cancer prediction that was proposed is still in question. Therefore, this research objective to improve the accuracy of machine learning methods through pre-processing: Missing Value Replacement, Data Transformation, Smoothing Noisy Data, Feature Selection / Attribute Weighting, Data Validation, and Unbalanced Class Reduction which is more efficient for Breast Cancer prediction. The results of this study propose several approaches: C4.5 - Z-Score - Genetic Algorithm for Breast Cancer Dataset with 77,27% accuracy, 7-Nearest Neighbor - Min-Max Normalization - Particle Swarm Optimization for Wisconsin Breast Cancer Dataset - Original with 97,85% accuracy, Artificial Neural Network - Z-Score - Forward Selection for Wisconsin Breast Cancer Dataset - Diagnostics with 98,24% accuracy, and 11-Nearest Neighbor - Min-Max Normalization - Particle Swarm Optimization for Wisconsin Breast Cancer Dataset - Prognostic with 83,33% accuracy. The performance of these approaches is better than standard/normal machine learning methods and the proposed methods by the best of previous related studies.  Kanker Payudara merupakan kanker yang paling sering ditemukan pada wanita dan tingkat kematiannya masih berada pada posisi dua di antara penyakit kanker laiinya. Akurasi yang tinggi dari pendekatan machine learning yang diusulkan oleh penelitian-penelitian terkait sering dicapai. Namun tanpa pra-pengolahan yang efisien, maka model prediksi Kanker Payudara yang diusulkan masih diragukan. Oleh karena itu, penelitian ini bertujuan untuk meningkatkan kinerja akurasi metode-metode machine learning melalui pra-pengolahan: Missing Value Replacement, Data Transformation, Smoothing Noisy Data, Feature Selection / Attribute Weighting, Data Validation, dan Unbalanced Class Reduction yang lebih efisien untuk prediksi Kanker Payudara. Hasil penelitian ini mengusulkan pendekatan: C4.5 – Z-Score – Genetic Algorithm untuk Breast Cancer Dataset dengan akurasi 77,27%, 7-Nearest Neighbor – Min-Max Normalization – Particle Swarm Optimization untuk Wisconsin Breast Cancer Dataset - Original dengan akurasi 97,85%, Artificial Neural Network – Z-Score – Forward Selection untuk Wisconsin Breast Cancer Dataset – Diagnostic dengan akurasi 98,24%, dan 11-Nearest Neighbor – Min-Max Normalization – Particle Swarm Optimization untuk Wisconsin Breast Cancer Dataset – Prognostic dengan akurasi 83,33%. Kinerja pendekatan-pendekatan tersebut lebih baik dari metode-metode machine learning standar/normal dan metode yang diusulkan penelitian-penelitian terkait sebelumnya yang terbaik. &nbsp