PERFORMANCE ANALYSIS OF MACHINE LEARNING TECHNIQUES USED FOR THE PREDICTION OF BREAST CANCER
Author(s):
B. Lavanya, I. Dilshad Banu
Keywords:
Breast cancer; Prediction; Treatment; Early diagnosis; Support vector machine
Abstract
Breast cancer is one of the most common malignancies in women, although it is extremely rare in men. According to the World Health Organization (WHO), cancer is characterized by the uncontrolled aberrant proliferation of cells in any organ or tissue of the human body. The probability of saving a patient with breast cancer largely depends on its detection and initiation of treatment, which leads to high survival rates and low treatment costs. Therefore, an accurate cancer prediction model that can diagnose breast cancer at an early stage is needed. Since machine learning algorithms can be used to most accurately predict breast cancer.This study presents an overview of the capabilities of many machinelearning algorithms, including Support Vector Machine (SVM), Light Gradient Boosting (LGB), Decision tree (C4.5), Naive Bayes, Random Forest, and K-Means algorithms with techniques selection of elements and without them. We used publicly available breast cancer datasets to test several approaches for autonomous tumour categories. After obtaining the results, a performance evaluation and comparison are carried out between these different classifiers. Thus, the best classification accuracy is achieved by the proposed Support vector machine algorithm, which has a maximum accuracy of 0.98% according to the experimental results.
Article Details
Unique Paper ID: 156504

Publication Volume & Issue: Volume 9, Issue 4

Page(s): 10 - 16
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