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.
The healthcare industry is a vast field with a plethora of data about patients,added to the huge medical records every passing day. In terms of science, this industry is 'information rich' yet 'knowledge poor'. However, data mining with its various analytical tools and techniques plays a major role in reducing the use of cumbersome tests used on patients to detect a disease. The aim of this paper is to employ and analyze different data mining techniques for the prediction of heart disease in a patient through extraction of interesting patterns from the dataset using vital parameters. This paper strives to bring out the methodology and implementation of these techniques-Artificial Neural Networks, Decision Tree and Naive Bayes and stress upon the results and conclusion induced on the basis of accuracy and time complexity. By far, the observations reveal that Artificial Neural Networks outperformed Naive Bayes and Decision Tree.
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