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Modified Multinomial Naïve Bayes Algorithm for Heart Disease Prediction

  • T. MarikaniEmail author
  • K. ShyamalaEmail author
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 33)

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.

Keywords

Data Mining Classification algorithm Naïve Bayes Python Multinomial Naïve Bayes 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Department of Computer ScienceSree Muthukumaraswamy CollegeChennaiIndia
  2. 2.Department of Computer ScienceDr. Ambedkar Govt. Arts College (Autonomous)ChennaiIndia

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