搜索资源列表
ML
- 关于机器学习分类算法应用的一个例子，主要是根据给定数据对良/恶性乳腺癌肿瘤预测的实际应用，如何使用分类算法(An example of the application of the machine learning classification algorithm is mainly based on the practical application of the given data to the prediction of ben
模式识别代码
- 基于matlab的Iris、乳腺癌数据集的模式识别分类算法，含有 遗传算法+SVM、isodata、感知器算法、LMSE、神经网络等算法的实现代码，用于聚类效果良好，是模式识别大作业的参考资料(The pattern recognition classification algorithm based on MATLAB for Iris and breast cancer data sets contains the implemen
PCA
- 对一个数据表格的特征进行提取，从而得出哪些特征对乳腺癌肿瘤的影响效果最大(The characteristics of a data table are extracted to find out which characteristics have the greatest impact on breast cancer.)
Breast_cancer_detection
- 关于威斯康星乳腺癌数据的分类程序，直接可用。需要打标。(The classification procedure for breast cancer data in Wisconsin is directly available. You need to make a bid.)
apriori-medical
- 借助乳腺癌患者的病理信息，挖掘患者症状与中医症状之间的关联关系；为治疗提供依据，挖掘潜在的病症因素。(With the help of pathological information of breast cancer patients, the relationship between patient symptoms and TCM symptoms can be excavated.)
uci-breast-cancer-master
- 机器学习中的随机森林算法，用于空气质量预测(Random forest algorithm in machine learning for air quality prediction)
Liver-Tumors-master
- 首先，本代码针对肝脏CT图像序列的特点，提出了一个肝癌图像分割算法，对医学图像的分割、辅助医生进行肝脏切割手术、为医生进行诊断和治疗、肝脏的三维重建、确定病变区域的大小及程度等也有着重要意义。(Firstly, according to the characteristics of liver CT image sequence, this code proposes a liver cancer image segmentation
classifier_D
- 使用SVM分类器来预测乳腺癌病人的预后(特征选择；分类器构建)，评价模型时使用无被交叉验证，性能评价指标包括准确率，AUC，灵敏度，特异度。学会最基本的机器学习方法。可查看分发给大家的代码，以后遇到类似的问题，可用相似的思路和代码。(The SVM classifier was used to predict the prognosis of breast cancer patients (feature selection; clas
precdict
- 基于神经网络对癌症的预测 预测结果 多特征线性回归预测癌症(Prediction of Cancer Based on Neural Network Multi-feature Linear Regression)
class 1
- rule discovery for cancer detection by particle swarm optimization in data mining
Metastatic status of sentinel lymph nodes in breast cancer determined with photoacoustic microscopy via dual-targeting n
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