IMPROVING HEART DISEASE PREDICTION USING FEATURE SELECTION APPROACHES

Abstract

Heart Disease is the disorder of heart and bloodveins. It is very difficult for medical practitioners and doctors to predict accurate about heart disease diagnosis. Data science is one of the more important things in early prediction and solves large data problems now days. This research paper describes the prediction of heart disease in medical field by using data science. As many researches done research related to that problem but the accuracy of prediction is still needed to be improved. So, this research focuses on feature selection techniques and algorithms where multiple heart disease datasets are used for experimentation analysis and to show the accuracy improvement. By using the Rapid miner as tool; Decision Tree, Logistic Regression, Logistic Regression SVM, Naïve Bayes and Random Forest; algorithms are used as feature selection techniques and improvement is shown in the results by showing the accuracy.

Existing System

Back propagation multilayer perception (MLP) of Artificial Neural Network

Proposed System

The proposed approach used to complete this research is started by downloading an open source UCI data set. After verifying the dataset, next step is preprocessing and data discretization in the form of Data cleaning, Data Transformation, Data Reduction, Binning and Select Attributes. After applying all these techniques on downloaded dataset, the main technique feature selection is applied. Later on, following algorithms are applied on the data i.e Decision Tree, Logistic regression, Logistic regression SVM, Naïve Bayes and Random forest to detect the desired output.

Architecture

BLOCK DIAGRAM