Knearst Algorithm Analysis – Neighbor Breast Cancer Prediction Coimbra

I. G. Prahmana, Kristina Annatasia Br Sitepu
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Abstract

A process to explain the results of the KNN algorithm analysis with the prediction of Breast Cancer Coimbra disease (Breast Cancer). The prediction output of the KNN algorithm will be added with the Simple Linear Regression algorithm modeling to measure the predictive data through a straight line as an illustration of the correlation relationship between 2 or more variables. Linear regression prediction is used as a technique for the relationship between variables in the prediction process of the Breast Cancer Coimbra data set (Breast Cancer). for the value of K in analyzing the KNN algorithm, take the nearest neighbor with the ranking results with K = 5 nearest neighbors which are taken in the KNN calculation. Which is where the output of the KNN algorithm classification will be analyzed with the Simple Linear Regression algorithm with Dependent (Cause) and Independent (effect) variables. The test results determine that the patient has breast cancer and the number of predictions based on age with glucose means that the patient is predicted to have breast cancer. analyze the KNN algorithm with Simple Liner Regression modeling with Python programming language.
Knearst算法分析-邻近乳腺癌预测科英布拉
介绍了KNN算法分析结果与乳腺癌科英布拉病(Breast Cancer)预测的过程。KNN算法的预测输出将加入简单线性回归算法建模,通过一条直线度量预测数据,以说明2个或多个变量之间的相关关系。在乳腺癌科英布拉数据集(Breast Cancer)的预测过程中,采用线性回归预测技术对变量之间的关系进行预测。对于分析KNN算法时K的值,取KNN计算中取K = 5个最近邻排序结果的最近邻。在这里,KNN算法分类的输出将使用具有因变量(Cause)和自变量(effect)的简单线性回归算法进行分析。测试结果确定患者患有乳腺癌,基于年龄的血糖预测数量意味着该患者被预测患有乳腺癌。用Python编程语言对KNN算法进行简单线性回归建模分析。
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