An Efficient and Robust Model for Cervical Cancer Risk Classification based on Random Forest Classifier

N. Meenakshisundaram, G. Ramkumar
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Abstract

As per the World Health Organization, cervical cancer is the fourth most common type of cancer that has a high fatality rate. This disease affects women all over the world, particularly in low-income and middle-income nations. Cancer of the cervix is one of the types of cancer that most freq uently strikes women and affects their reproductive organs. It takes place when cells that are normally found in the cervix transform into malignant cells. The human papillomavirus (HPV), which is spread through sexual activity, is the most important risk factor for developing cervical cancer. There is a significant amount of interest in machine learning, and scientists generally examine its application in every possible setting. Using a Random Forest classifier, the primary purpose of this work is to categorize the clinical dataset of cervical cancer to determine the type of cervical cancer test. Because the dataset is unbalanced and is lacking a significant amount of value, it must be going through an intensive data pre-processing phase. The effectiveness of categorization was tested were quantified using confusion matrices. This was done to establish the efficiency power of every categorization technique.
基于随机森林分类器的高效鲁棒宫颈癌风险分类模型
根据世界卫生组织的数据,宫颈癌是死亡率高的第四大常见癌症。这种疾病影响世界各地的妇女,特别是低收入和中等收入国家的妇女。子宫颈癌是最常发生在妇女身上并影响其生殖器官的癌症之一。当宫颈内正常的细胞转变为恶性细胞时,就会发生这种情况。人乳头瘤病毒(HPV)是通过性行为传播的,是导致子宫颈癌的最重要危险因素。人们对机器学习非常感兴趣,科学家们通常会在每一个可能的环境中研究它的应用。使用随机森林分类器,本工作的主要目的是对宫颈癌临床数据集进行分类,以确定宫颈癌检测的类型。由于数据集是不平衡的,并且缺乏大量的价值,因此必须经过密集的数据预处理阶段。使用混淆矩阵对分类的有效性进行了量化测试。这样做是为了确定每种分类技术的效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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