Utilizing Various Machine-Learning Techniques in Breast Cancer Detection

Abstract

Worldwide, cancer is the most frequent cause of passing away for women. Any development in predicting and diagnosing cancer is crucial for a healthy life. As such, vital cancer accuracy in predicting patients' survival parameters and treatment aspects is necessary. Machine learning methods significantly impact breast cancer diagnosis and early diagnosis. This study aims to increase prediction accuracy using a novel statistical feature selection technique. This article examines the classification test accuracy, standard data precision, and the process performance of multiple machine learning (ML) algorithms, such as Random; using the Wisconsin Diagnostic Breast Cancer (WDBC) dataset, the following models were used: Support Vector Machine, Logistic Regression, Decision Tree (C4.5), Forest Naïve Bayes (NB), Linear Regression (LR), k-nearest-neighbors (KNN), and Multilayer Perceptron (MLP). The data set is partitioned to use the machine learning algorithm: 20% is used during the test phase, while 80% is used throughout training. Hyper-parameters that are manually assigned are utilized to modify the classifier. When applied to a subset of data, it showed that combining SVM and model in machine learning reached the maximum accuracy of up to 90%, which was noticeably superior to the other ML model.