Heart Disease Analysis Research Using K-Nearest Neighbor: a Review

EasyChair Preprint 10626

Abstract

Analysing complex data is called data mining. The process of deciding what will happen next is called forecasting. Many techniques for predictive analytics are used these days. Predictive analytics are performed using the SVM method. This technique splits the data into her two phases: testing and training. The 1st type of test data is primarily on people who have less or not at all of developing heart disease. The possible getting heart disease is over 50% in his second class of test data. In this work, we propose to improve existing methods using decision tree classifiers. This suggestion improves accuracy while reducing execution time.

Keyphrases: Decision Tree, KNN, SVM

@booklet{EasyChair:10626,
  author    = {Neelamani Samal and Manjinder Kaur and Rohit Kumar Singhal and Jai Sukh Paul Singh},
  title     = {Heart Disease Analysis Research Using K-Nearest Neighbor: a Review},
  howpublished = {EasyChair Preprint 10626},
  year      = {EasyChair, 2023}}