Title: Application of data mining techniques for early detection of heart diseases using Framingham heart study dataset

Authors: Nancy Masih; Sachin Ahuja

Addresses: Department of Computer Science and Engineering, Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India ' Department of Computer Science and Engineering, Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India

Abstract: Healthcare organisations accumulate large amount of healthcare data, but it is not 'extracted' to draw hidden patterns which can prove efficient for decision making process. Data mining techniques prove useful in gaining insights by discovering hidden patterns from the datasets which remain undetected manually. Heart diseases are the main cause of mortality rate in the globe. Hence, it is critical to predict the heart diseases at early stage with more accuracy and speed to save the millions of people's lives. This paper aims to examine and compare the accuracy of four different machine learning algorithms for predicting and diagnosing heart disease using Framingham heart study (FHS) dataset. The output of the study confirms the most prominent features that cause heart diseases and which must be analysed for early detection of the disease. This study will be used as prognostic information in treatment of heart diseases.

Keywords: heart disease; prediction; Framingham heart study; FHS; decision tree; naïve Bayes; support vector machine; SVM; artificial neural network.

DOI: 10.1504/IJBET.2022.123149

International Journal of Biomedical Engineering and Technology, 2022 Vol.38 No.4, pp.334 - 344

Received: 25 Oct 2018
Accepted: 21 Feb 2019
Published online: 01 Jun 2022 *

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