A Heart Disease Prediction Model using Logistic Regression By Cleveland DataBase

Abstract

The early prognosis of cardiovascular diseases can aid in making decisions to lifestyle changes in high risk patients and in turn reduce their complications. Research has attempted to pinpoint the most influential factors of heart disease as well as accurately predict the overall risk using homogenous data mining techniques. Recent research has delved into amalgamating these techniques using approaches such as hybrid data mining algorithms. This paper proposes a rule based model to compare the accuracies of applying rules to the individual results of logistic regression on the Cleveland Heart Disease Database in order to present an accurate model of predicting heart disease.

An Innovative Method for Predicting and Classifying Inadequate Accuracy in Heart Disease by Using Decision Tree with K-Nearest Neighbors Algorithm

“…However, the difference was not statistically significant (p<0.05). (Rani et al 2018) who reported that the dependent variable value varies by the total number of instants and parameters considered in the dataset and proves 78% accuracy using nonlinear methods in logistic regression. Non-linear logistic may normalize some of the key attributes in preprocessing where decision trees train the system with each attribute in the form of the tree structure and give more (98%) accuracy than the logistic regression.…”

“…26 papers from IEEE Xplore and 15 from google scholar have been selected and referred to in this paper. (Rani et al 2018) implemented multiple regression models with 70% training data and 30% testing data. with 13 different heart disease medical parameters, he concludes that his proposed algorithm is better than other algorithms.…”