Data Mining Techniques in Heart Disease Detection
International Journal of Computer Science (IJCS) Published by SK Research Group of Companies (SKRGC).
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Abstract
Mining information and knowledge from large databases has been recognized by many researchers as a key research topic in database systems and machine learning, and by many industrial companies as an important area with an opportunity of major revenues. Researchers in many different fields have shown great interest in data mining. Several emerging applications in information providing services, such as data warehousing and on-line services over the Internet. It also call for various data mining techniques to better understand user behavior, to improve the service provided, and to increase the business opportunities. This paper presents a majority voting ensemble method that is able to predict the possible presence of heart disease in humans. The prediction is based on simple affordable medical tests conducted in any local clinic. Moreover, the aim of this project is to provide more confidence and accuracy to the Doctor’s diagnosis since the model is trained using real-life data of healthy and ill patients. The model classifies the patient based on the majority vote of several machine learning models in order to provide more accurate solutions than having only one model. Finally, this approach produced an accuracy of 90the hard voting ensemble model.
References
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Keywords
Data Mining, Large Databases, Data Warehousing, Machine Learning.
