Heart Disease Detection with Data Analytics and Machine Learning

T Subburaj; Gagan G.R

doi:10.34293/sijash.v11iS1-July.6322

T Subburaj Department of Master of Computer Applications Rajarajeswari College of Engineering
Gagan G.R Department of Master of Computer Applications Rajarajeswari College of Engineering

DOI: https://doi.org/10.34293/sijash.v11iS1-July.6322

Keywords: Cardiology,, Data Analysis, Data Mining, Diseases, Pattern Classification, Support Vector Machine, Heart Disease Prediction, ArtificalNueral Network (ANN).

Abstract

ML(Machine Learning) has recently played an important part in the healthcare business, and among all It is important diseases, heart among the more typical crucial and vital diseases to forecast. The quantity of instances is rapidly increasing every day. Considering that it has been discovered four persons older than or equal to 30 and 50 have a stroke every minute, We are using methods of machine learning to alleviate this problem. The heartdisease dataset was utilised by Kaggle for this project. This research examines and shows the likelihood of developing utilising several machine learning classifying methods, like Naive Bayes, to identify cardiovascular diseases Random Forest, SVM, and others. Later on, the Stacking Ensemble Learning Technique is utilised in order boost efficiency of our classification models.