Pendekatan Machine Learning yang Efisien untuk Prediksi Kanker Payudara

Azminuddin I. S. Azis; Irma Surya Kumala Idris; Budy Santoso; Yasin Aril Mustofa

doi:10.29207/resti.v3i3.1347

Azminuddin I. S. Azis Universitas Ichsan Gorontalo
Irma Surya Kumala Idris Universitas Ichsan Gorontalo
Budy Santoso Universitas Ichsan Gorontalo
Yasin Aril Mustofa Universitas Ichsan Gorontalo

DOI: https://doi.org/10.29207/resti.v3i3.1347

Keywords: machine learning, breast cancer prediction, missing value replacement, feature selection, unbalanced class

Abstract

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.

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