Efficient Data Mining Techniques for Heart Disease Prediction and Comparative Analysis of Classification Algorithms
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Date
2020-12-30
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Abstract
Data mining techniques are used to extract interesting patterns and discover meaningful knowledge
from huge amount of data. There has been increasing in usage of data mining techniques on
medical data for determining useful trends and patterns that are used in analysis and decision
making. About eighty percent of human deaths occurred in low and middle-income countries due
to heart diseases. The healthcare industry generates large amount of heart disease data which are
not organized. These data make the prediction process more complicated and voluminous. Data
mining provides the techniques for fast and accurate transformation of data into useful information
for heart diseases prediction. The main objectives of this research is to predict heart diseases more
accurately using Naïve Bayes, Decision Tree, Neural Network, Random Forest classification
algorithms and compare the performance of classifiers. The research uses raw dataset for
performance analysis and the analysis is based on Jupyter. This research also shows better
classification technique from them which is Random Forest on the basis of accuracy.
Keywords: KDD, Jupyter, Naïve Bayes, Decision Tree, Multilayer Perceptron, CSV.