Mathematical Modeling of Cancers Using Machine Learning Algorithms

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Abstract

This paper shows a mathematical modeling method using different machine learning algorithms for prediction of probability of procuring Pancreatic Cancer (PC). Each algorithm reports its own accuracy, precision, recall and F1-score. Also, a Bayesian network model is used to determine the probability each subject has in contracting PC on the basis of certain preconditions, like his dietary habits and other biological attributes. This paper makes use of the PC dataset as provided by the National Cancer Institute in collaboration with National Institute of Health (NIH). The features obtained from this dataset can have either a binary value or a scalar value. The dataset consists of three questionnaires distributed to 155000 subjects. In each of these questionnaires, the subject is asked about his dietary habits and illness history.
使用机器学习算法的癌症数学建模
本文展示了一种使用不同机器学习算法预测胰腺癌(PC)发生概率的数学建模方法。每个算法报告自己的准确性、精密度、召回率和f1分数。此外,贝叶斯网络模型用于确定每个受试者在特定前提条件(如饮食习惯和其他生物属性)的基础上感染PC的概率。本文使用了由美国国立癌症研究所与美国国立卫生研究院(NIH)合作提供的PC数据集。从该数据集获得的特征可以是二进制值,也可以是标量值。数据集由三份问卷组成,共分发给155000名受试者。在这些问卷中,受试者被问及他的饮食习惯和病史。
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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