WCSE

Heart Disease Prediction Model Based on Logistic Regression Theory

Abstract— The establishment of accurate and effective disease prediction model is of great practical significance to the medical community. In this context, this paper proposes a heart disease prediction model based on logistic regression theory. Firstly, the acquired data sets are transformed according to the characteristics of logical regression theory; secondly, the data are normalized to eliminate the possible influence of different dimensions on the simulation results; finally, the simulation experiments are carried out based on UCI data set to analyze the characteristics that affect the prediction accuracy of the model. The experimental results show that the accuracy and recall rate of the model can reach 82.33% and 95.83% respectively, which can be used to assist doctors in diagnosis and treatment of heart disease.

Index Terms— predictive model; logistic regression; classification; heart disease.

Bai Wei
Xi'an University of Posts and Telecommunications
Teng Jinbao
Xi'an University of Posts and Telecommunications

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Cite: Bai Wei, Teng Jinbao, " Heart Disease Prediction Model Based on Logistic Regression Theory, " WCSE 2022 Spring Event: 2022 9th International Conference on Industrial Engineering and Applications, pp. 1501-1508, Sanya, China, April 15-18, 2022.