Open AccessCardiovascular disease prediction using deep learning techniques
Author(s) -
Syed Nawaz Pasha,
Dadi Ramesh,
Sallauddin Mohmmad,
A. Harshavardhan,
N.A. Shabana
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/981/2/022006
Subject(s) - support vector machine , artificial intelligence , computer science , machine learning , artificial neural network , reliability (semiconductor) , disease , deep learning , k nearest neighbors algorithm , decision tree , data mining , medicine , power (physics) , physics , quantum mechanics
Cardiovascular Disease or coronary illness is one of the significant dangerous infections in India as well as in the entire world. It is estimated that 28.1 % of deaths occur due to heart diseases. It is also the major cause for significant number of deaths which as more than 17.6 million in the year 2016. So proper and timely diagnosis, treatment of such diseases require a system that can predict with precise accuracy and reliability. Intensive research is carried out by various researchers using diverse machine learning algorithms to forecast the heart disease taking different datasets which consists of different attributes that result in heart attack. In this paper we analyzed the dataset collected from kaggle which consists of attributes related to heart disease such as age, gender, blood pressure, cholesterol and so on. We have also investigated the accuracy levels of various machine learning techniques such as Support Vector Machines (SVM), K-Nearest Neighbor (KNN), Decision Trees (DT). The performance and accuracy of above algorithms is not so well when executed using large dataset, so here we tried to improving the prediction accuracy using Artificial Neural Network(ANN), Tensor Flow Keras.