Abstract
There are number of challenging research areas available in the field of medical technologies. Among them cardio-vascular disease prediction plays a vital role. By applying data mining techniques, valuable knowledge can be extracted from the health care system. In this proposed work heart disease can be detected by using a classifier algorithm. The world health organization has projected 17.7 million people died from CVDs in 2015, representing 31% of all global deaths. According to this survey, it is anticipated that nearly 7.4 million people will die due to coronary heart disease and 6.7 million were due to stroke. The proposed algorithm was Modified Multinomial Naïve Bayes algorithms (MMNB). This algorithm helps us to predict the heart disease more accurately compared to other supervised algorithm. The proposed algorithm provides 74.8% of accuracy which is better than the Naïve Bayes Algorithm.
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Marikani, T., Shyamala, K. (2020). Modified Multinomial Naïve Bayes Algorithm for Heart Disease Prediction. In: Balaji, S., Rocha, Á., Chung, YN. (eds) Intelligent Communication Technologies and Virtual Mobile Networks. ICICV 2019. Lecture Notes on Data Engineering and Communications Technologies, vol 33. Springer, Cham. https://doi.org/10.1007/978-3-030-28364-3_27
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DOI: https://doi.org/10.1007/978-3-030-28364-3_27
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