Abstract
Globally, the medical industry is presumably “information rich” and “knowledge poor”. KDD, i.e. knowledge discovery from data is hence, applied to extract interesting patterns from the dataset using different data mining techniques. This massive data available is essential for the extraction of useful information and generate relationships amongst the attributes. The aim of this paper is to compile, tabulate and analyze the different data mining techniques that have been implied and implemented in the recent years for Heart Disease Prediction. Each previous paper exhibits a set of strengths and limitations in terms of the data types used in the dataset, accuracy, ease of interpretation, reliability and generalization ability. This paper strives to bring out stark comparisons and put light to the pros and cons of each of the techniques. By far, the observations reveal that Neural Networks performed well as compared to Naive Bayes and Decision Tree considering appropriate conditions.
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Chadha, R., Mayank, S., Vardhan, A., Pradhan, T. (2016). Application of Data Mining Techniques on Heart Disease Prediction: A Survey. In: Shetty, N., Prasad, N., Nalini, N. (eds) Emerging Research in Computing, Information, Communication and Applications. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2553-9_38
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DOI: https://doi.org/10.1007/978-81-322-2553-9_38
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