Application of data mining techniques for early detection of heart diseases using Framingham heart study dataset Online publication date: Wed, 01-Jun-2022
by Nancy Masih; Sachin Ahuja
International Journal of Biomedical Engineering and Technology (IJBET), Vol. 38, No. 4, 2022
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.
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