Comparison and analysis of the effect of various machine learning algorithms on cancer prediction

Abstract

Cancer is a serious disease that can have a big impact on the physical and mental health of patients. The incidence and mortality rate of cancer are increasing globally. Therefore, predicting the occurrence and treatment effect of cancer has become a hot research topic in the medical field. Machine learning algorithms can use vast amounts of data and algorithmic models to predict aspects of cancer occurrence, progression, and treatment effectiveness. This algorithm can learn and find patterns from large amounts of medical data, thereby improving the diagnosis and treatment of cancer. In this paper, naive Bayes model, logistic regression model, random forest model, KNN model and Ridge regression model were used to predict the data of cancer patients. The accuracy of naive Bayes model and logistic regression model is the highest, reaching 95%. The accuracy of random forest model and KNN model is 94%. The worst performing model was Ridge regression, with an accuracy of 93%. Naive Bayes algorithm and logistic regression algorithm perform well in solving binary classification problems because they are both probability-based classification algorithms. In this problem, the number of features is large, so the ridge regression algorithm can effectively process the data, but due to the small amount of data, it is easy to appear underfitting, so the performance is poor. In this paper, a variety of machine learning algorithms are used to predict cancer patients, and the accuracy, accuracy, recall rate and f1 parameters of each model are calculated and compared, which provides a basis for subsequent research.