Construction of a prediction model for heart disease: a study based on random forest, KNN and logistic regression

The development of computer technology has brought new breakthroughs to the medical field. Taking heart disease as an example, as one of the main causes of death worldwide, its high incidence rate and mortality have brought enormous pressure the social and public health system. In recent years, with the rise of big data and artificial intelligence technologies, using machine learning in computers to construct predictive models has become a new trend in cardiovascular disease risk This article utilizes computer technology to establish random forest, KNN, and logistic regression models for training and predicting heart disease data. In order to evaluate the predictive performance of these computer models, this paper selected evaluation indicators such as accuracy, precision, recall, F1 score, and AUC, and conducted experiments on the original test set data, standardized test set data, and normalized test set data, respectively. The experimental results show that these machine learning models exhibit high accuracy and reliability in heart disease diagnosis, and can assist doctors in making more accurate judgments.