Research on Intelligent Algorithm of Cancer Diagnosis Evaluation Based on Machine Learning

Scientific and efficient intelligent classification model is an important basis for cancer evaluation and diagnosis. Traditional cancer diagnosis and evaluation methods are greatly affected by human subjective factors. Support vector machine can classify linearly indivisible data sets very well, but it is easy to misclassify sample points that are close to the classification hyperplane. The nearest neighbor algorithm can also classify the samples well, and it is not sensitive to outliers in the classification process, but the calculation is large. According to the advantages and disadvantages of support vector machine model and nearest neighbor model, the sample points that are close to the classification hyperplane in support vector machine are classified by the nearest neighbor model, that is, the SVM-KNN combination model is proposed, so as to train an intelligent classification diagnosis model with high classification accuracy and is effective and feasible. A breast cancer data set was used for empirical analysis, and the combined model improved the classification accuracy of the data set.