基于多层感知器算法的宫颈癌预测

S. Sujanthi, A. S, H. K, S. S
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引用次数: 0

摘要

妇女第四大与疾病有关的死亡原因是宫颈恶性生长。宫颈癌与人乳头瘤病毒(HPV)的存在有关。早期发现可以预防宫颈癌,从而降低了该疾病的总体影响。由于常规检查费用高昂,缺乏意识,以及医疗设施有限,在不发达国家,妇女很少参加足够的筛查项目。这样一来,每个病人都将面临极高的风险。有许多威胁因素可导致子宫颈癌的发展。因此,将使用各种数据分析工具(包括机器学习和深度学习算法)检查数据集是否患有宫颈癌。正常和异常子宫颈数据的分类是通过快速概述子宫颈癌的工作原理和检测方法来完成的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction of Cervical Cancer using Multilayer Perceptron Algorithm
The fourth most frequent illness-related cause of death in women is cervical malignant growth. Cervical cancer is associated with the presence of the human papillomavirus (HPV). Early detection has made cervical cancer preventable, which has decreased overall impact of the disease. Due to the high expense of routine exams, a lack of awareness, and limited access to medical facilities, women do not participate in enough screening programs in underdeveloped countries. This way, each patient is expected to be at extremely high risk. There are numerous threat factors that can lead to the growth of cervical cancer. As a result, the datasets will be checked for cervical cancer by using a variety of data analytics tools, including machine learning and deep learning algorithms. The classification of normal and abnormal cervical data is done by performing a quick overview of how cervical cancer works and is detected.
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