Open AccessHeart Disease Risk Predictor
Author(s) -
Rashmi Mishra,
Pooja Saharan,
Amrita Jyoti
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.j8872.0881019
Subject(s) - computer science , decision tree , data mining , machine learning , bayesian network , naive bayes classifier , set (abstract data type) , sorting , artificial neural network , decision support system , artificial intelligence , population , probabilistic logic , support vector machine , disease , raw data , data science , medicine , environmental health , pathology , programming language
Cardiovascular disease is one of the focused areas is medical area because its origins sickness and death amongst the population of the entire world. Data mining techniques play an important role to convert the large amount of raw data into meaningful information which will help in prediction and decision of Cardiovascular disease. The prediction models were technologically advanced using diverse amalgamation structures and sorting techniques such as k-NN, Naive Bayes, LR, SVM, Neural Network, Decision Tree. It is very necessary for the recital of the prediction models to choose the exact amalgamation of momentous features. The main Aim of the propose System is to develop an develop an Intelligent System using data mining modeling technique. The proposed system retrieves the data set and compare the data set with the predefined trained data set. The existing decision support system cannot predict the complex question for diagnosing the heart disease but the proposed system predicts the complex queries which will help and assist the healthcare practitioners to take appropriate decisions. This proposed system aims to provide a web platform to predict the occurrences of disease on the basis of various symptoms. The user can select various symptoms and can find the diseases with their probabilistic figures.