PREDICTIVE ANALYSIS OF HEART DISEASE USING K-MEANS AND APRIORI ALGORITHMS
Résumé
Disease prediction is one of the most important issues we face today. The research paper aims to develop a Data-Mining based console that analyzes large amounts of data and extracts information that can be converted into useful knowledge. However, there is a lack of effective analytical tools to discover hidden data relationships and trends that even the doctors are unable to predict the disease accurately. There is a need to develop an effective decision system that can use the available data to correctly predict the disease. So, in this paper we are introducing an automated console that predicts heart disease through clustering K-means and Apriori algorithms in combination with MATLAB software. These two algorithms can be used to effectively detect the stage of the disease. The stages of heart disease predicted are low risk, medium risk, and high risk stages.
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