基于机器学习的乳腺癌分类预测,Computational Intelligence and Neuroscience

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基于机器学习的乳腺癌分类预测
Computational Intelligence and Neuroscience Pub Date : 2023-1-11 , DOI: 10.1155/2023/6530719
Hua Chen ₁ , Nan Wang ₁ , Xueping Du ₁ , Kehui Mei ₁ , Yuan Zhou ₁ , Guangxing Cai ₁

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乳腺癌是世界上最常见和致命的癌症类型。本文基于XGBoost、随机森林、逻辑回归、K近邻等机器学习算法，建立不同的模型对乳腺癌进行分类和预测，为乳腺癌的早期诊断提供参考。Recall表示医学诊断中检出恶性癌细胞的概率，对乳腺癌的分类具有重要意义，因此本文以recall为首要评价指标，综合考虑precision、accuracy、F1-score评价指标，对各个模型的预测效果进行评价和比较。为了消除不同维度概念对模型效果的影响，对数据进行了标准化处理。为了找到最优子集，提高模型的准确率，通过皮尔逊相关检验筛选出15个特征作为模型的输入。K近邻模型采用交叉验证的方法选择最优的k以召回率作为评价指标的价值。针对正负样本不平衡的问题，采用分层抽样的方法，按照不同的类别按比例抽取训练集和测试集。实验结果表明，在不同的数据集划分（8:2和7:3）下，同一模型的预测效果会有不同的变化。对比分析表明，本文建立的XGBoost模型（将训练集和测试集按8:2划分）效果较好，其召回率、查准率、准确率、F 1-score分别为1.00、0.960、0.974、分别为 0.980。

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Classification Prediction of Breast Cancer Based on Machine Learning

Breast cancer is the most common and deadly type of cancer in the world. Based on machine learning algorithms such as XGBoost, random forest, logistic regression, and K-nearest neighbor, this paper establishes different models to classify and predict breast cancer, so as to provide a reference for the early diagnosis of breast cancer. Recall indicates the probability of detecting malignant cancer cells in medical diagnosis, which is of great significance for the classification of breast cancer, so this article takes recall as the primary evaluation index and considers the precision, accuracy, and F1-score evaluation indicators to evaluate and compare the prediction effect of each model. In order to eliminate the influence of different dimensional concepts on the effect of the model, the data are standardized. In order to find the optimal subset and improve the accuracy of the model, 15 features were screened out as input to the model through the Pearson correlation test. The K-nearest neighbor model uses the cross-validation method to select the optimal k value by using recall as an evaluation index. For the problem of positive and negative sample imbalance, the hierarchical sampling method is used to extract the training set and test set proportionally according to different categories. The experimental results show that under different dataset division (8 : 2 and 7 : 3), the prediction effect of the same model will have different changes. Comparative analysis shows that the XGBoost model established in this paper (which divides the training set and test set by 8 : 2) has better effects, and its recall, precision, accuracy, and F1-score are 1.00, 0.960, 0.974, and 0.980, respectively.

更新日期：2023-01-11

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