Kernel-Based Naive Bayes Classifier for Medical Predictions

  • Dishant KhannaEmail author
  • Arunima Sharma
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 695)


Researchers and clinical practitioners in medicine are working on predictive data analysis at an alarming rate. Classification methods developed using different modeling methodologies is an active area of research. In this paper, live dataset in clinical medicine is used to implement recent work on predictive data analysis, implementing a kernel-based Naïve Bayes classifier in order to validate some learned lessons for predicting the possible disease. With the medical diagnosis prediction, the aim is to enable the physician to report the disease, which might be true. The input training dataset for the classifier was taken from a government hospital.


Classifiers Naïve Bayes Kernel density Modeling Live dataset Prediction Precision Kernel Naïve Bayes 



This work has been possible thanks to Ms. Narina Thakur, Head of Department, Department of Computer Science Engineering, BVCOE, New Delhi.


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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Bharati Vidyapeeth’s College of EngineeringNew DelhiIndia
  2. 2.Columbia UniversityNew YorkUSA

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