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Predictive Data Mining for Cardiopathy

Siddhant Sanadhaya, Sandeep Tuli

Abstract


Large amounts of statistics and data is collected by the healthcare industry on a daily basis. Unfortunately, the important and crucial ones are hidden and are “dug” data Information to conclude important and efficient decisions. Complex hidden patterns and relationships are generally unexplored. Cutting-edge Data Mining approach can be used in resolving this particular condition. This study is proposing a prototype model called Cardiopathy Prediction System (CPS) using data mining methods, namely Decision Trees, Naive Bayes and the Neural Network. Each of the three models is effective in achieving its objectives in its own way. CPS can provide a complex "what if" answer Questions that classic decision support systems are not capable of. Using the Medical statistics which includes various attributes for example sex, blood pressure, sugar and age patients predict heart disease risk Disease. This allows important knowledge, like Samples, Correlation among medical aspects associated with cardiopathy is formed and can be used. CPS is user-friendly, scalable, dependable, and expandable.


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References


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